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Tag: conversational AI

Why Even the Best Conversational AI Chatbot Will Fail Your CX

Posted on Mark Kowal

As author, speaker, and customer experience expert Dan Gingiss wrote in his book The Experience Maker, “Most companies must realize that they are no longer competing against the guy down the street or the brand that sells similar products. Instead, they’re competing with every other experience a customer has.”

That’s why so many CX leaders were (cautiously!) optimistic when Generative AI (GenAI) hit the scene, promising to provide instant, round-the-clock responses and faster issue resolutions, automate personalization at scale, and free agents to focus on more complex issues. So much so that a whopping 80% of companies worldwide now have chatbots on their websites.

Yet despite all the hype and good intentions, a recent survey showed that consumers give their chatbot experiences an average rating of 6.4/10 — which isn’t a passing grade in school, and certainly won’t cut it in business.

So why have chatbots fallen so short of company and consumer expectations? The short answer is because they’re not AI agents. Chatbots rely on rigid, rule-based systems. They struggle to understand context and adapt to complex or nuanced questions. Even the best conversational AI chatbot doesn’t have what it takes to enable CX leaders to create seamless customer journeys. This is why they so often fail at driving outcomes like revenue and CSAT.

Let’s look at the most impactful differences between these two AI for CX solutions, including why even the best conversational AI chatbots are failing CX teams and their customers — and how AI agents are changing the game.

Chatbots: First-generation AI and Intent-based Responses

AI is advancing at lightning speed, so it should come as no surprise that many vendors are having trouble keeping up. The truth is that most AI for CX tools still offer chatbots built on first-generation AI, rather than AI agents that are powered by the latest and greatest Large Language Models (LLMs).

This first-generation AI is rule-based and uses Natural Language Processing (NLP) to attempt to match users’ questions to specific, pre-defined queries and responses. In other words, CX teams must create lists of different ways users might pose the same question or request, or “intents.” AI does its best to determine which “intent” a user’s message aligns with, and then sends what has been labeled the “correct” corresponding response.

Best Conversational AI Chatbot

This approach can cause many problems that ultimately add friction to the customer journey and create frustrating brand experiences, including:

  • Intent limitations: If a user asks a multi-part question (e.g. “Can I unsubscribe from your newsletter and have sales contact me?”), the bot will recognize and answer only one intent and ignore the other, which is insufficient.
  • Ridged paths: If a user asks a question that the bot knows requires additional information, it will start the user down a rigid, predefined path to collect that information. If the user provides additional relevant details (e.g. “I would still like to receive customer-only emails”), the bot will continue to push them down this specific path before providing an answer.
    On the other hand, if the user asks an unrelated follow-up question, the bot will zero in on this new “intent” and start the user down a new path, abandoning the previous flow without resolving their original inquiry.
  • Confusing intents: There are countless ways to phrase the same request, so the likelihood of a user’s inquiry not matching a predefined intent is high (e.g. “I want you to delete my contact info!”). In this case, the bot doesn’t know what to do and must escalate to a live agent — or worse, it misunderstands the user’s intent and sends the wrong response.
  • Conflicting intents: Because similar words and phrases can appear across unrelated issues, there is often contention across predefined intents (e.g. “I accidentally unsubscribed from your newsletter.”). Even the best conversational AI chatbot is likely to match the user’s intent with the wrong response and deliver an unrelated and seemingly nonsensical answer — an issue similar to hallucinations.

Some AI for CX vendors claim their chatbots use the most advanced GenAI. However, they are really using only a fraction of an LLM’s power to generate a response from a knowledge base, rather than crafting personalized answers to specific questions. But because they still use the same outdated, intent-based process to determine the user’s request, the LLM will still struggle to generate a sufficient, appropriate response — if the issue isn’t escalated to a live agent first, that is.

AI Agents: Cutting-edge Models with Reasoning Capabilities

Top AI for CX vendors use the latest and greatest LLMs to power every step of the customer interaction, not just at the end to generate a response. This results in a much more accurate, personalized, and empathetic experience, enabling them to provide clients with AI agents — not chatbots.

Best Conversational AI Chatbot

Rather than relying on rigid intent classification, AI agents use LLMs to comprehend language and genuinely understand a user’s request, much like humans do. They can also contextualize the question and append the conversation with additional attributes accessed from other CX systems, such as a person’s location or whether they are an existing customer (more on that in this guide).

This level of reasoning is achieved through business logic, which guides the conversation flow through a series of “pre-generation checks” that happen in the background in mere seconds. These require the LLM to first answer “questions about the question” before generating a response, including if the request is in scope, sensitive in nature, about a specific product or service, or requires additional information to answer effectively.

Sidenote! 

The best AI for CX vendors never use client data to train LLMs to “invent” answers to questions about their products or services. Instead, the LLMs must generate responses using information from specific, trusted knowledge sources that the client has pre-approved. 

This means AI agents harness the language and communication capabilities of GenAI only, greatly reducing the need for CX leaders to worry about data security or hallucinations. You can go here to learn more.

 

Best Conversational AI Chatbot

The same process happens after the LLM has generated a response (“post-generation checks”), where the LLM must answer “questions about the answer” to ensure that it’s accurate, in context, on brand, etc. Leveraging the reasoning power of LLMs coupled with this conversational framework enables the AI agent to outperform even the best conversational AI chatbots in many key areas.

Providing sufficient answers to multi-part questions

Unlike a chatbot, the agent is not trying to map a specific question to a single, canned answer. Instead, it’s able to interpret the entirety of the user’s question, identify all relevant knowledge, and combine it to generate a comprehensive response that directly answers the user’s inquiry.

Dynamically answering unrelated questions and factoring in new information

AI agents will prompt users to provide additional information as needed to effectively respond to their requests. However, if the user volunteers additional information, the agent will factor this into the context of the larger conversation, rather than continuing to force them down a step-by-step path like a chatbot does. This effectively bypasses the need for many disambiguating questions.

Similarly, if a user asks an unrelated follow-up question, the agent will respond to the question without losing sight of the original inquiry, providing answers and maintaining the flow of the conversation while still collecting the information it needs to solve the original issue.

Understanding nuances

Unlike chatbots, next-gen AI agents excel at comprehending human language and picking up on nuances in user questions. Rather than having to identify a user’s intent and match it with the correct, predefined response, they can recognize that similar requests can be phrased differently, and that dissimilar questions may contain many of the same words. This allows them to flexibly understand users’ questions and identify the right knowledge to generate an accurate response without requiring an exact match.

Best Conversational AI Chatbot

It’s also worth noting that first-generation AI vendors often force clients to build a new chatbot for every channel: voice, SMS, Facebook Messenger, etc. Not only does this mean a lot of duplicate work for internal teams on the back end, but it can also lead to disjointed brand experiences on the front end. In contrast, next-generation AI for CX vendors allows clients to build a single agent and run it across multiple channels for a more seamless customer journey.

Is Your “Best-in-Class” AI Chatbot Killing Your Customer Journey?

Some 80% of customers say the experience a company provides is equally as important as its products and services. However, according to Gartner, more than half of large organizations have failed to unify customer engagement channels and provide a streamlined experience across them.

As you now know, even the best conversational AI chatbot will exacerbate rather than improve this issue. Our latest guide deep dives into more ways your chatbot is harming CX, from offering multi-channel-only support to measuring the wrong things, as well as the steps you can take to provide consumers with a more seamless journey. You can give it a read here!

The Ultimate Guide to RCS Business Messaging

Posted on Max Fortis

From chiseling words into stone to typing them directly on our screens, changes in technology can bring profound changes to the way we communicate. Rich Communication Services (RCS) Business Messaging is one such technological change, and it offers the forward-looking contact center a sophisticated upgrade over traditional SMS.

In this piece, we’ll discuss RCS Business Messaging, illustrating its significance, its inner workings, and how it can be leveraged as part of a broader customer service strategy. This context will equip you to understand RCS and determine whether and how to invest in it.

Let’s get going!

What is RCS Business Messaging?

Smartphones have become enormously popular for surfing the internet, shopping, connecting with friends, and conducting many other aspects of our daily lives. One consequence of this development is that it’s much more common for contact centers to interact with customers through text messaging.

Once text messaging began to replace phone calls, emails, and in-person visits as the go-to communication channel, it was clear that it required an upgrade. The old Short Messaging Service (SMS) was replaced with Rich Communication Services (RCS), which supports audio messages, video, high-quality photos, group chats, encryption, and everything else we’ve come to expect from our messaging experience.

And, on the whole, the data indicate that this is a favorable trend:

  • More than 70% of people report feeling inclined to make an online purchase when they have the ability to get timely answers to questions;
  • Almost three-quarters indicated that they were more likely to interact with a brand when they have the option of doing so through RCS;
  • Messages sent through RCS are a staggering 35 times more likely to be read than an equivalent email.

For all these reasons, your contact center needs to be thinking about how RCS fits into your overall customer service strategy–it’s simply not a channel you can afford to ignore any longer.

How is RCS Business Messaging Different from Google Business Messages?

Distinguishing between Google’s Rich Communication Services (RCS) and Google Business Messages can be tricky because they’re similar in many ways. That said, keeping their differences in mind is crucial.

You may not remember this if you’re young enough, but text messaging was once much more limited. Texts could not be very long, and were unable to accommodate modern staples like GIFs, videos, and emojis. However, as reliance on text messaging grew, there was a clear need to enhance the basic protocol to include these and other multimedia elements.

Since this enhancement enriched the basic functionality of text messaging, it is known as “rich” communication. Beyond adding emojis and the like, RCS is becoming essential for businesses looking to engage in more dynamic interactions with customers. It supports features such as custom logos, collecting data for analytics, adding QR codes, and links to calendars or maps, and enhancing the messaging experience all around.

Google Business Messages, on the other hand, is a mobile messaging channel that seamlessly integrates with Google Maps and Search to deliver high-quality, asynchronous communication between your customers and your contact center agents.

This service is not only a boon to your satisfaction ratings, it can also support other business objectives by reducing the volume of calls and enhancing conversion rates.

While Google Business Messages and RCS have a lot in common, there are two key differences worth highlighting: RCS is not universally available across all Android devices (whereas Business Messages is), and Business Messages does not require a user to install a messaging app (whereas RCS does).

Learn More About the End of Google Business Messages

 

How Does RCS Business Messaging Work?

Okay, now that we’ve convinced you that RCS Business Messaging is worth the effort to cultivate, let’s examine how it works.

Once you set up your account and complete the registration process, you’ll need to create an “agent,” which is the basic interface connecting your contact center to your customers. Agents are quite flexible and able to handle very simple workflows (such as sending a notification) as well as much more complicated sequences of tasks (such as those required to help book a reservation).

From the customer’s side, communicating with an agent is more or less indistinguishable from having a standard conversation. Each participant will speak in turn, waiting for the other to respond.

Agents can be configured to initiate a conversation under a wide variety of external circumstances. They could reach out when a user’s order has been shipped, for example, or when a new sushi restaurant has opened and is offering discounts. Since we’re focused on contact centers, our agent configurations will likely revolve around events like “the customer reached out for support,” “there’s been an update on an outstanding ticket,” or “the issue has been resolved.”

However you’ve chosen to set up your agent, when it is supposed to initiate a conversation, it will use the RCS Business Messaging API to send a message. These messages are always sent as standard HTTP requests with a corresponding JSON payload (if you’re curious about the technical underpinnings), but the most important thing to know is that the message ultimately ends up in front of the user, where they can respond.

Unless, that is, their device doesn’t support RCS. RCS has become popular and prominent enough that we’d be surprised if you ran into this situation very often. Just in case, you should have your messaging set up such that you can default to something like SMS.

Any subsequent messages between the agent and the customer are also sent as JSON. Herein lies the enormous potential for customization, because you can utilize powerful technologies like natural language understanding to have your agent dynamically generate different responses in different contexts. This not only makes it feel more lifelike, it also means that it can solve a much broader range of problems.

If you don’t want to roll up your sleeves and do this yourself, you always have the option of partnering with a good conversational AI platform. Ideally, you’d want to use one that makes integrating generative AI painless, and which has a robust set of features that make it easy to monitor the quality of agent interactions, collect data, and make decisions quickly.

Best Practices for Using RCS Business Messaging

By now, you should hopefully understand RCS Business Messaging, why it’s exciting, and the many ways in which you can use it to take your contact center to new heights. In this penultimate section, we’ll discuss some of Google’s best practices for RCS.

RCS is not a General-Purpose User Interface

Tools are incredibly powerful ways of extending basic human abilities, but only if you understand when and how to use them. Hammers are great for carpentry, but they’re worse than useless when making pancakes (trust us on this–we’ve tried, and it went poorly).

The same goes for Google’s RCS Business Messaging, which is a conversational interface. Your RCS agents are great at resolving queries, directing customers to information, executing tasks, and (failing that) escalating to a human being. But in order to do all of this, you should try to make sure they speak in a way that is natural, restricted to the question at hand, and easy for the customer to follow.

For this same reason, your agents shouldn’t be seen as a simple replacement for a phone tree, requiring the user to tediously input numbers to navigate a menu of delimited options. Part of the reason agents are a step forward in contact center management is precisely because they eliminate the need to lean on such an approach.

Check Device Compatibility Beforehand

Above, we pointed out that some devices don’t support RCS, and you should therefore have a failsafe in place if you send a message to one. This is sage advice, but it’s also possible to send a “capability request” ahead of a message telling you what kind of device the user has and what messaging it supports.

This will allow you to configure your agent in advance so that it stays within the limits of a given device.

Begin at the Beginning

As you’ve undoubtedly heard from marketing experts, first impressions matter a lot. The way your agent initiates a conversation will determine the user’s experience, and thereby figure prominently in how successful you are in making them happy.

In general, it’s a good idea to have the initial message be friendly, warm, and human, to contain some of the information the user is likely to want, and to list out a few of the things the agent is capable of. This way, the person who reached out to you with a problem immediately feels more at ease, knowing they’ll be able to reach a speedy resolution.

Be Mindful of Technical Constraints

There are a few low-level facts about RCS that could bear on the end user’s experience, and you should know about them as you integrate RCS into your text messaging strategy.

To take one example, messages containing media may process more slowly than text-only messages. This means that you could end up with messages getting out of order if you send several of them in a row.

For this reason, you should wait for the RBM platform to return a 200 OK response for each message before proceeding to send the next. This response indicates the platform has received the message, ensuring users receive them as intended.

Additionally, it’s important to be on the lookout for duplicate incoming messages. When receiving messages from users, always check the `messageId` to confirm that the message hasn’t been processed before. By keeping track of `messageId` strings, duplicate messages can be easily identified and disregarded, ensuring efficient and accurate communication.

Integrate with Quiq

RCS is the next step in text messaging, opening up many more ways of interacting with the people reaching out to you for help.

There are many ways to leverage RCS, one of which is turbo-charging your agents with the power of large language models. The easiest way to do this is to team up with a conversation AI platform to do the technical heavy lifting for you.

Quiq is one such platform. Reach out to schedule a demo with us today!

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How Large Language Models Have Evolved

Posted on The Quiq Team

In late 2022, large language models (LLMs) exploded into public awareness almost overnight. But like most overnight sensations, the history of large language models is long, fascinating, and informative.

In this piece, we’ll trace the deep evolution of language models and use this as a lens into how they can change your contact center today–and in the future.

Let’s get started!

A Brief History of Artificial Intelligence Development

The human fascination with building artificial beings capable of thought and action goes back a long way. Writing in roughly the 8th century BCE, Homer recounted tales of the Greek god Hephaestus outsourcing repetitive manual tasks to automated bellows and working alongside robot-like “attendants” that were “…golden, and in appearance like living young women.”

Some 500 years later, mathematicians in Alexandria would produce treatises on creating mechanical servants and various kinds of automata. Heron wrote a technical manual for producing a mechanical shrine and an automated theater whose figurines could stage a full tragic play.

Nor is it only ancient Greece that tells similar tales. Jewish legends speak of the Golem, a being made of clay and imbued with life and agency through language. The word “abracadabra”, in fact, comes from the Aramaic phrase “avra k’davra,” which translates to “I create as I speak.”

Through the ages, these old ideas have found new expression in stories such as “The Sorcerer’s Apprentice,” Mary Shelley’s “Frankenstein,” and Karel Čapek’s “R.U.R.,” a science fiction play that features the first recorded use of the word “robot.”

From Science Fiction to Science Fact

But they remained purely fiction until the early 20th Century – a pivotal moment in the history of LLMs – when advances in the theory of computation and the development of primitive computers began to offer a path to building intelligent systems.

Arguably, this really began in earnest with the 1950 publication of Alan Turing’s “Computing Machinery and Intelligence” – in which he proposed the famous “Turing test” – and with the 1956 Dartmouth conference on AI, organized by luminaries John McCarthy and Marvin Minsky.

People began taking AI seriously. Over the next ~50 years in the evolution of large language models, there were numerous periods of hype and exuberance in which major advances were made and long “AI winters” in which funding dried up, and little was accomplished.

Three advances acted to really bring LLMs into their own: the development of neural networks, the deep learning revolution, and the rise of big data. These are important for understanding the history of large language models, so it’s to these that we now turn.

Neural Networks and the Deep Learning Revolution

Walter Pitts and Warren McCulloch laid the groundwork for the eventual evolution of language models in the early 1940s. Inspired by the burgeoning study of the human brain, they wondered if it would be possible to build an artificial neuron with some of the same basic properties as a biological one.

They were successful, though several other breakthroughs would be required before artificial neurons could be arranged into systems capable of doing useful work. One such breakthrough was the discovery of backpropagation in 1960, the basic algorithm still used to train deep learning systems.

It wasn’t until 1985, however, that David Rumelhart, Ronald Williams, and Geoff Hinton used backpropagation in neural networks; in 1989, this allowed Yann LeCun to train such a network to recognize handwritten digits.

Ultimately, it would be these deep neural networks (DNNs) that would emerge from the history of LLMs as the dominant paradigm, but for completeness, we should briefly mention some of the methods that it replaced.

One was known as “rule-based approaches,” and it was exactly what it sounded like. Early AI assistants would be programmed directly with grammatical rules, which were used to parse text and craft responses. This was just as limiting as you’d imagine, and the approach is rarely seen today except in the most straightforward of cases.

Then, there were statistical language models, which bear at least a passing resemblance to the behemoth LLMs that came later. These models try to predict the probability of word n given the n-1 words that came before. If you read our deep dive on LLMs, this will sound familiar, though it was not at all as powerful and flexible as what’s available today.

There were others that are beyond the scope of this treatment, but the key takeaway is that gargantuan neural networks ended up winning the day.

To close this section out, we’ll mention a handful of architectural improvements that came out of this period and would play a crucial role in the evolution of language models. We’ll focus on two in particular: transformers and word vector embeddings.

If you’ve investigated how LLMs work, you’ve probably heard both terms. Transformers are famously intricate, but the basic idea is that they creatively combined elements of predecessor architectures to ameliorate the problems those approaches faced. Specifically, they can use self-attention to selectively attend to key pieces of information in text, allowing them to render higher-fidelity translations and higher-quality text generations.

Word vector embeddings are numerical representations of words that capture underlying semantic information. When interacting with ChatGPT, it can be easy to forget that computers don’t actually understand language, they understand numbers. A word vector embedding is an array of numbers generated with one of several different algorithms, with similar words having similar embeddings. LLMs can process these embeddings to learn enormous statistical patterns in unstructured linguistic data, then use those patterns to generate their own outputs.

All of this research went into making the productive neural networks that are currently changing the nature of work in places like contact centers. The last missing piece was data, which we’ll cover in the next section.

The Big Data Era

Neural networks and deep-learning applications tend to be extremely data-hungry, and access to quality training data has always been a major bottleneck. In 2009 Stanford’s Fei-Fei Li sought to change this by releasing Imagenet, a database of over 14 million labeled images that could be used for free by researchers. The increase in available data, together with substantial improvements in computer hardware like graphical processing units (GPUs), meant that at long last the promise of deep learning could begin to be fulfilled.

And it was. In 2011, a convolutional neural network called “AlexNet” won multiple international competitions for image recognition, IBM’s Watson system beat several Jeopardy! all-stars in a real game, and Apple launched Siri. Amazon’s Alexa followed in 2014, and from 2015 to 2017 DeepMind’s AlphaGo shocked the world by utterly dominating the best human Go players.

All of this set the stage for the rise of LLMs just four short years later.

Where are we Now in the Evolution of Large Language Models?

Now that we’ve discussed this history, we’re well-placed to understand why LLMs and generative AI have ignited so much controversy. People have been mulling over the promise (and peril) of thinking machines for literally thousands of years, and it looks like they might finally be here.

But what, exactly, has people so excited? What is it that advanced AI tools are doing that has captured the popular imagination? In the following sections, we’ll talk about the astonishing (and astonishingly rapid) improvements seen in language models in recent memory.

Getting To Human-Level

One of the more surprising things about LLMs such as ChatGPT is just how good they are at so many different things. LLMs are trained by having them take samples of the text data they’re given, and then trying to predict what words come next given the words that came before.

Modern LLMs can do this incredibly well, but what is remarkable is just how far this gets you. People are using generative AI to help them write poems, business plans, and code, create recipes based on the ingredients in their fridges, and answer customer questions.

What is Emergence in Language Models?

Perhaps even more interesting, however, is the phenomenon of emergence in language models. When researchers tested LLMs on a wide variety of tasks meant to be especially challenging to these models – things like identifying a movie given a string of emojis or finding legal chess moves – they found that in about 5% of tasks, there is a sudden, sharp increase in ability on a given task once a model reaches a certain size.

At present, it’s not really clear how we should think about emergence. One hypothesis for emergence is that a big enough model is able to learn some general piece of knowledge not attainable by a smaller sibling, while another, more prosaic one is that it’s a relatively straightforward consequence of the model’s internal statistical machinery.

What’s more, it’s difficult to pin down the conditions required for emergence in language models. Though it generally appears to be a function of model size, there are cases in which the same abilities can be achieved with smaller models, or with models trained on very high-quality data, and emergence shows up at different scales for different models and tasks.

Whatever ends up being the case, it’s clear that this is a promising direction for future research. Much more work needs to be done to understand how precisely LLMs accomplish what they accomplish. This will not only redound upon the question of emergence, it will also inform the ongoing efforts to make language models safer and less biased.

LLM Agents

One of the bigger frontiers in LLM research is the creation of agents. ChatGPT and similar platforms can generate API calls and functioning code, but humans still need to copy and paste the code to actually do anything with it.

Agents are meant to get around this limitation. Auto-GPT, for example, pairs an underlying LLM with a “bot” that takes high-level tasks, breaks them down into tasks an LLM can solve, and stitches together those solutions.

This work is still in its infancy, but it continues to be very promising.

Multimodal Models

Another development worth mentioning is the rise of multi-modality. A model is “multi-modal” when it can process more than one kind of information, like images and text.

LLMs are staggeringly good at producing coherent language, and image models could do the same thing with images, but now a lot of time and effort is being spent on combining these two kinds of functionality.

The result has been models able to find specific sections of lengthy videos, generate images to accompany textual explanations, and create their own incredible videos from short, simple prompts.

It’s too early to tell what this will mean, but it’s already impacting branding, marketing, and related domains.

What’s Next For Large Language Models?

As with so many things, the meteoric rise of LLMs was presaged by decades of technical work and thousands of years of thought and speculation. In just a few short years, it has become the strategic centerpiece for contact centers the world over.

If you want to get in on the action, you could start by learning more about how Quiq builds customer-facing AI assistants using LLMs. This will provide the context you need to make the wisest decision about deploying this remarkable technology.

Brand Voice And Tone Building With Prompt Engineering

Posted on The Quiq Team

Artificial intelligence tools like ChatGPT are changing the way strategists are building their brands.

But with the staggering rate of change in the field, it can be hard to know how to utilize its full potential. Should you hire an engineering team? Pay for a subscription and do it yourself?

The truth is, it depends. But one thing you can try is prompt engineering, a term that refers to carefully crafting the instructions you give to the AI to get the best possible results.

In this piece, we’ll cover the basics of prompt engineering and discuss the many ways in which you can build your brand voice with generative AI.

What is Prompt Engineering?

As the name implies, generative AI refers to any machine learning (ML) model whose primary purpose is to generate some output. There are generative AI applications for creating new images, text, code, and music.

There are also ongoing efforts to expand the range of outputs generative models can handle, such as a fascinating project to build a high-level programming language for creating new protein structures.

The way you get output from a generative AI model is by prompting it. Just as you could prompt a friend by asking “How was your vacation in Japan,” you can prompt a generative model by asking it questions and giving it instructions. Here’s an example:

“I’m working on learning Java, and I want you to act as though you’re an experienced Java teacher. I keep seeing terms like `public class` and `public static void`. Can you explain to me the different types of Java classes, giving an example and explanation of each?”

When we tried this prompt with GPT-4, it responded with a lucid breakdown of different Java classes (i.e., static, inner, abstract, final, etc.), complete with code snippets for each one.

When Small Changes Aren’t So Small

Mapping the relationship between human-generated inputs and machine-generated outputs is what the emerging field of “prompt engineering” is all about.

Prompt engineering only entered popular awareness in the past few years, as a direct consequence of the meteoric rise of large language models (LLMs). It rapidly became obvious that GPT-3.5 was vastly better than pretty much anything that had come before, and there arose a concomitant interest in the best ways of crafting prompts to maximize the effectiveness of these (and similar) tools.

At first glance, it may not be obvious why prompt engineering is a standalone profession. After all, how difficult could it be to simply ask the computer to teach you Chinese or explain a coding concept? Why have a “prompt engineer” instead of a regular engineer who sometimes uses GPT-4 for a particular task?

A lot could be said in reply, but the big complication is the fact that a generative AI’s output is extremely dependent upon the input it receives.

An example pulled from common experience will make this clearer. You’ve no doubt noticed that when you ask people different kinds of questions you elicit different kinds of responses. “What’s up?” won’t get the same reply as “I notice you’ve been distant recently, does that have anything to do with losing your job last month?”

The same basic dynamic applies to LLMs. Just as subtleties in word choice and tone will impact the kind of interaction you have with a person, they’ll impact the kind of interaction you have with a generative model.

All this nuance means that conversing with your fellow human beings is a skill that takes a while to develop, and that also holds in trying to productively using LLMs. You must learn to phrase your queries in a way that gives the model good context, includes specific criteria as to what you’re looking for in a reply, etc.

Honestly, it can feel a little like teaching a bright, eager intern who has almost no initial understanding of the problem you’re trying to get them to solve. If you give them clear instructions with a few examples they’ll probably do alright, but you can’t just point them at a task and set them loose.

We’ll have much more to say about crafting the kinds of prompts that help you build your brand voice in upcoming sections, but first, let’s spend some time breaking down the anatomy of a prompt.

This context will come in handy later.

What’s In A Prompt?

In truth, there are very few real restrictions on how you use an LLM. If you ask it to do something immoral or illegal it’ll probably respond along the lines of “I’m sorry Dave, but as a large language model I can’t let you do that,” otherwise you can just start feeding it text and seeing how it responds.

That having been said, prompt engineers have identified some basic constituent parts that go into useful prompts. They’re worth understanding as you go about using prompt engineering to build your brand voice.

Context

First, it helps to offer the LLM some context for the task you want done. Under most circumstances, it’s enough to give it a sentence or two, though there can be instances in which it makes sense to give it a whole paragraph.

Here’s an example prompt without good context:

“Can you write me a title for a blog post?”

Most human beings wouldn’t be able to do a whole lot with this, and neither can an LLM. Here’s an example prompt with better context:

“I’ve just finished a blog post for a client that makes legal software. It’s about how they have the best payments integrations, and the tone is punchy, irreverent, and fun. Could you write me a title for the post that has the same tone?”

To get exactly what you’re looking for you may need to tinker a bit with this prompt, but you’ll have much better chances with the additional context.

Instructions

Of course, the heart of the matter is the actual instructions you give the LLM. Here’s the context-added prompt from the previous section, whose instructions are just okay:

“I’ve just finished a blog post for a client that makes legal software. It’s about how they have the best payments integrations, and the tone is punchy, irreverent, and fun. Could you write me a title for the post that has the same tone?”

A better way to format the instructions is to ask for several alternatives to choose from:

“I’ve just finished a blog post for a client that makes legal software. It’s about how they have the best payments integrations, and the tone is punchy, irreverent, and fun. Could you give me 2-3 titles for the blog post that have the same tone?”

Here again, it’ll often pay to go through a couple of iterations. You might find – as we did when we tested this prompt – that GPT-4 is just a little too irreverent (it used profanity in one of its titles.) If you feel like this doesn’t strike the right tone for your brand identity you can fix it by asking the LLM to be a bit more serious, or rework the titles to remove the profanity, etc.

You may have noticed that “keep iterating and testing” is a common theme here.

Example Data

Though you won’t always need to get the LLM input data, it is sometimes required (as when you need it to summarize or critique an argument) and is often helpful (as when you give it a few examples of titles you like.)

Here’s the reworked prompt from above, with input data:

“I’ve just finished a blog post for a client that makes legal software. It’s about how they have the best payments integrations, and the tone is punchy, irreverent, and fun. Could you give me 2-3 titles for the blog post that have the same tone?

Here’s a list of two titles that strike the right tone:
When software goes hard: dominating the legal payments game.
Put the ‘prudence’ back in ‘jurisprudence’ by streamlining your payment collections.”

Remember, LLMs are highly sensitive to what you give them as input, and they’ll key off your tone and style. Showing them what you want dramatically boosts the chances that you’ll be able to quickly get what you need.

Output Indicators

An output indicator is essentially any concrete metric you use to specify how you want the output to be structured. Our existing prompt already has one, and we’ve added another (both are bolded):

“I’ve just finished a blog post for a client that makes legal software. It’s about how they have the best payments integrations, and the tone is punchy, irreverent, and fun. Could you give me 2-3 titles for the blog post that have the same tone? Each title should be approximately 60 characters long.

Here’s a list of two titles that strike the right tone:
When software goes hard: dominating the legal payments game.
Put the ‘prudence’ back in ‘jurisprudence’ by streamlining your payment collections.”

As you go about playing with LLMs and perfecting the use of prompt engineering in building your brand voice, you’ll notice that the models don’t always follow these instructions. Sometimes you’ll ask for a five-sentence paragraph that actually contains eight sentences, or you’ll ask for 10 post ideas and get back 12.

We’re not aware of any general way of getting an LLM to consistently, strictly follow instructions. Still, if you include good instructions, clear output indicators, and examples, you’ll probably get close enough that only a little further tinkering is required.

What Are The Different Types of Prompts You Can Use For Prompt Engineering?

Though prompt engineering for tasks like brand voice and tone building is still in its infancy, there are nevertheless a few broad types of prompts that are worth knowing.

  • Zero-shot prompting: A zero-shot prompt is one in which you simply ask directly for what you want without providing any examples. It’ll simply generate an output on the basis of its internal weights and prior training, and, surprisingly, this is often more than sufficient.
  • One-shot prompting: With a one-shot prompt, you’re asking the LLM for output and giving it a single example to learn from.
  • Few-shot prompting: Few-shot prompts involve a least a few examples of expected output, as in the two titles we provided our prompt when we asked it for blog post titles.
  • Chain-of-thought prompting: Chain-of-thought prompting is similar to few-shot prompting, but with a twist. Rather than merely giving the model examples of what you want to see, you craft your examples such that they demonstrate a process of explicit reasoning. When done correctly, the model will actually walk through the process it uses to reason about a task. Not only does this make its output more interpretable, but it can also boost accuracy in domains at which LLMs are notoriously bad, like addition.

What Are The Challenges With Prompt Engineering For Brand Voice?

We don’t use the word “dazzling” lightly around here, but that’s the best way of describing the power of ChatGPT and the broader ecosystem of large language models.

You would be hard-pressed to find many people who have spent time with one and come away unmoved.

Still, challenges remain, especially when it comes to using prompt engineering for content marketing or building your brand voice.

One well-known problem is the tendency of LLMs to completely make things up, a phenomenon referred to as “hallucination”. The internet is now filled with examples of ChatGPT completely fabricating URLs, books, papers, professions, and individuals. If you use an LLM to create content for your website and don’t thoroughly vet it, you run the risk of damaging your reputation and your brand if it contains false or misleading information.

A related problem is legal or compliance issues that emerge as a result of using an LLM. Though the technology hasn’t been around long enough to get anyone into serious trouble (we suspect it won’t be long), there are now cases in which attorneys have been caught using faulty research generated by ChatGPT or engineering teams have leaked proprietary secrets by feeding meeting notes into it.

Finally, if you’re offering a fine-tuned model to customers to do something like answer questions, you must be very, very careful in delimiting its scope so that it doesn’t generate unwanted behavior. It’s pretty easy to accidentally wander into fraught territory when engaging with an LLM in an open-ended manner, and that’s not even counting users who deliberately try to get it to respond inappropriately.

One potential solution to this problem is by crafting your prompts such that they contain clear instructions about what not to do. You may tell it not to discuss its own rules, not to change its tone, not to speak negatively about anyone, not to argue, etc.

Crafting a prompt that illustrates the correct behavior while explicitly ruling out any incorrect behaviors is a non-trivial task, requiring a great deal of testing and refinement. But it’s one you’ll have to get right if you want to leverage LLMs for your brand voice while avoiding any possible harm down the line.

Prompt Engineering For Marketers

With all this context having been established, you’re no doubt excited about using prompt engineering in your marketing efforts. In this section, we’ll go through a couple of ways you can do that.

Coming up with ideas

One place where LLMs excel is in helping you with ideation. A lot goes into content marketing, but all of that follows from having a solid list of basic concepts to work from.

We tested ChatGPT’s functionality by feeding it this query:

“I’m starting a blog about becoming a data scientist. Could you give me a list of 10 blog post ideas, with a mix of beginner, intermediate, and advanced topics? The titles should be eye-catching but not too aggressive.

Here are two examples of good blog post titles:

‘Data science: what to know when considering a career in this hot new field.’
‘Data science v.s. data engineering: everything you wanted to know’”

And we got back some excellent ideas:

  1. “Demystifying Data Science: A Beginner’s Guide to Jumpstarting Your Career”
  2. “From Novice to Ninja: Essential Skills for Every Data Science Aspirant”
  3. “Unlocking the Power of Python: A Data Scientist’s Best Friend”
  4. “Exploratory Data Analysis: Unveiling Insights Hidden in the Numbers”
  5. “Machine Learning 101: A Gentle Introduction for Data Science Enthusiasts”
  6. “Feature Engineering: Enhancing Your Models Like a Pro”
  7. “The Art of Natural Language Processing: Extracting Meaning from Text”
  8. “Unsupervised Learning: Unveiling Patterns and Structure in Your Data”
  9. “Deep Dive into Neural Networks: Building Advanced Models for Predictive Analytics”
  10. “Data Science in the Real World: Tackling Complex Business Problems with Analytics”

And look, it even generated the correct number of titles! You’ll notice, though that every single one follows the same template, with an initial phrase, a colon, and then a follow-on phrase. That’s because the two examples I gave have this structure, and that’s what it used as a guide.

Performing Background Research

After you’re done coming up with good ideas for building your brand voice, you’ll likely want to do some additional background research on your domain. This, too, is a place where LLMs excel.

There can be a lot of subtlety to this. You might start with something obvious, like “give me a list of the top authors in the keto diet niche”, but you can also get more creative than this. We’ve heard of copywriters who have used GPT-3.5 to generate lengthy customer testimonials for fictional products, or diary entries for i.e. 40-year-old suburban dads who are into DIY home improvement projects.

Regardless, with a little bit of ingenuity, you can generate a tremendous amount of valuable research that can inform your attempts to develop a brand voice.

Be careful, though; this is one place where model hallucinations could be really problematic. Be sure to manually check a model’s outputs before using them for anything critical.

Generating Actual Content

Of course, one place where content marketers are using LLMs more often is in actually writing full-fledged content. We’re of the opinion that GPT-3.5 is still not at the level of a skilled human writer, but it’s excellent for creating outlines, generating email blasts, and writing relatively boilerplate introductions and conclusions.

Getting better at prompt engineering

Despite the word “engineering” in its title, prompt engineering remains as much an art as it is a science. Hopefully, the tips we’ve provided here will help you structure your prompts in a way that gets you good results, but there’s no substitute for practicing the way you interact with LLMs.

One way to approach this task is by paying careful attention to the ways in which small word choices impact the kinds of output generated. You could begin developing an intuitive feel for the relationship between input text and output text by simply starting multiple sessions with ChatGPT and trying out slight variations of prompts. If you really want to be scientific about it, copy everything over into a spreadsheet and look for patterns. Over time, you’ll become more and more precise in your instructions, just as an experienced teacher or manager does.

Prompt Engineering Can Help You Build Your Brand

Advanced AI models like ChatGPT are changing the way SEO, content marketing, and brand strategy are being done. From creating buyer personas to using chatbots for customer interactions, these tools can help you get far more work done with less effort.

But you have to be cautious, as LLMs are known to hallucinate information, change their tone, and otherwise behave inappropriately.

With the right prompt engineering expertise, these downsides can be ameliorated, and you’ll be on your way to building a strong brand. If you’re interested in other ways AI tools can take your business to the next level, schedule a demo of Quiq’s conversational CX platform today!

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LLMs For the Enterprise: How to Protect Brand Safety While Building Your Brand Persona

Posted on J.R. Rettenmeyer

It’s long been clear that advances in artificial intelligence change how businesses operate. Whether it’s extremely accurate machine translation, chatbots that automate customer service tasks, or spot-on recommendations for music and shows, enterprises have been using advanced AI systems to better serve their customers and boost their bottom line for years.

Today the big news is generative AI, with large language models (LLMs) in particular capturing the imagination. As we’d expect, businesses in many different industries are enthusiastically looking at incorporating these tools into their workflows, just as prior generations did for the internet, computers, and fax machines.

But this alacrity must be balanced with a clear understanding of the tradeoffs involved. It’s one thing to have a language model answer simple questions, and quite another to have one engaging in open-ended interactions with customers involving little direct human oversight.

If you have an LLM-powered application and it goes off the rails, it could be mildly funny, or it could do serious damage to your brand persona. You need to think through both possibilities before proceeding.

This piece is intended as a primer on effectively using LLMs for the enterprise. If you’re considering integrating LLMs for specific applications and aren’t sure how to weigh the pros and cons, it will provide invaluable advice on the different options available while furnishing the context you need to decide which is the best fit for you.

How Are LLMs Being Used in Business?

LLMs like GPT-4 are truly remarkable artifacts. They’re essentially gigantic neural networks with billions of internal parameters, trained on vast amounts of text data from books and the internet.

Once they’re ready to go, they can be used to ask and answer questions, suggest experiments or research ideas, write code, write blog posts, and perform many other tasks.

Their flexibility, in fact, has come as quite a surprise, which is why they’re showing up in so many places. Before we talk about specific strategies for integrating LLMs into your enterprise, let’s walk through a few business use cases for the technology.

Generating (or rewriting) text

The obvious use case is generating text. GPT-4 and related technologies are very good at writing generic blog posts, copy, and emails. But they’ve also proven useful in more subtle tasks, like producing technical documentation or explaining how pieces of code work.

Sometimes it makes sense to pass this entire job on to LLMs, but in other cases, they can act more like research assistants, generating ideas or taking human-generated bullet points and expanding on them. It really depends on the specifics of what you’re trying to accomplish.

Conversational AI

A subcategory of text generation is using an LLM as a conversational AI agent. Clients or other interested parties may have questions about your product, for instance, and many of them can be answered by a properly fine-tuned LLM instead of by a human. This is a use case where you need to think carefully about protecting your brand persona because LLMs are flexible enough to generate inappropriate responses to questions. You should extensively test any models meant to interact with customers and be sure your tests include belligerent or aggressive language to verify that the model continues to be polite.

Summarizing content

Another place that LLMs have excelled is in summarizing already-existing text. This, too, is something that once would’ve been handled by a human, but can now be scaled up to the greater speed and flexibility of LLMs. People are using LLMs to summarize everything from basic articles on the internet to dense scientific and legal documents (though it’s worth being careful here, as they’re known to sometimes include inaccurate information in these summaries.)

Answering questions

Though it might still be a while before ChatGPT is able to replace Google, it has become more common to simply ask it for help rather than search for the answer online. Programmers, for example, can copy and paste the error messages produced by their malfunctioning code into ChatGPT to get its advice on how to proceed. The same considerations around protecting brand safety that we mentioned in the ‘conversational AI’ section above apply here as well.

Classification

One way to get a handle on a huge amount of data is to use a classification algorithm to sort it into categories. Once you know a data point belongs in a particular bucket you already know a fair bit about it, which can cut down on the amount of time you need to spend on analysis. Classifying documents, tweets, etc. is something LLMs can help with, though at this point a fair bit of technical work is required to get models like GPT-3 to reliably and accurately handle classification tasks.

Sentiment analysis

Sentiment analysis refers to a kind of machine learning in which the overall tone of a piece of text is identified (i.e. is it happy, sarcastic, excited, etc.) It’s not exactly the same thing as classification, but it’s related. Sentiment analysis shows up in many customer-facing applications because you need to know how people are responding to your new brand persona or how they like an update to your core offering, and this is something LLMs have proven useful for.

What Are the Advantages of Using LLMs in Business?

More and more businesses are investigating LLMs for their specific applications because they confer many advantages to those that know how to use them.

For one thing, LLMs are extremely well-suited for certain domains. Though they’re still prone to hallucinations and other problems, LLMs can generate high-quality blog posts, emails, and general copy. At present, the output is usually still not as good as what a skilled human can produce.

But LLMs can generate text so quickly that it often makes sense to have the first draft created by a model and tweaked by a human, or to have relatively low-effort tasks (like generating headlines for social media) delegated to a machine so a human writer can focus on more valuable endeavors.

For another, LLMs are highly flexible. It’s relatively straightforward to take a baseline LLM like GPT-4 and feed it examples of behavior you want to see, such as generating math proofs in the form of poetry (if you’re into that sort of thing.) This can be done with prompt engineering or with a more sophisticated pipeline involving the model’s API, but in either case, you have the option of effectively pointing these general-purpose tools at specific tasks.

None of this is to suggest that LLMs are always and everywhere the right tool for the job. Still, in many domains, it makes sense to examine using LLMs for the enterprise.

What Are the Disadvantages of Using LLMs in Business?

For all their power, flexibility, and jaw-dropping speed, there are nevertheless drawbacks to using LLMs.

One disadvantage of using LLMs in business that people are already familiar with is the variable quality of their output. Sometimes, the text generated by an LLM is almost breathtakingly good. But LLMs can also be biased and inaccurate, and their hallucinations – which may not be a big deal for SEO blog posts – will be a huge liability if they end up damaging your brand.

Exacerbating this problem is the fact that no matter how right or wrong GPT-4 is, it’ll format its response in flawless, confident prose. You might expect a human being who doesn’t understand medicine very well to misspell a specialized word like “Umeclidinium bromide”, and that would offer you a clue that there might be other inaccuracies. But that essentially never happens with an LLM, so special diligence must be exercised in fact-checking their claims.

There can also be substantial operational costs associated with training and using LLMs. If you put together a team to build your own internal LLM you should expect to spend (at least) hundreds of thousands of dollars getting it up and running, to say nothing of the ongoing costs of maintenance.

Of course, you could also build your applications around API calls to external parties like OpenAI, who offer their models’ inferences as an endpoint. This is vastly cheaper, but it comes with downsides of its own. Using this approach means being beholden to another entity, which may release updates that dramatically change the performance of their models and materially impact your business.

Perhaps the biggest underlying disadvantage to using LLMs, however, is their sheer inscrutability. True, it’s not that hard to understand at a high level how models like GPT-4 are trained. But the fact remains that no one really understands what’s happening inside of them. It’s usually not clear why tiny changes to a prompt can result in such wildly different outputs, for example, or why a prompt will work well for a while before performance suddenly starts to decline.

Perhaps you just got unlucky – these models are stochastic, after all – or perhaps OpenAI changed the base model. You might not be able to tell, and either way, it’s hard to build robust, long-range applications around technologies that are difficult to understand and predict.

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How Can LLMs Be Integrated Into Enterprise Applications?

If you’ve decided you want to integrate these groundbreaking technologies into your own platforms, there are two basic ways you can proceed. Either you can use a 3rd-party service through an API, or you can try to run your own models instead.

In the following two sections, we’ll cover each of these options and their respective tradeoffs.

Using an LLM through an API

An obvious way of leveraging the power of LLMs is by simply including API calls to a platform that specializes in them, such as OpenAI. Generally, this will involve creating infrastructure that is able to pass a prompt to an LLM and return its output.

If you’re building a user-facing chatbot through this method, that would mean that whenever the user types a question, their question is sent to the model and its response is sent back to the user.

The advantages of this approach are that they offer an extremely low barrier to entry, low costs, and fast response times. Hitting an API is pretty trivial as engineering tasks go, and though you’re charged per token, the bill will surely be less than it would be to stand up an entire machine-learning team to build your own model.

But, of course, the danger is that you’re relying on someone else to deliver crucial functionality. If OpenAI changes its terms of service or simply goes bankrupt, you could find yourself in a very bad spot.

Another disadvantage is that the company running the model may have access to the data you’re passing to its models. A team at Samsung recently made headlines when it was discovered they’d been plowing sensitive meeting notes and proprietary source code directly into ChatGPT, where both were viewable by OpenAI. You should always be careful about the data you’re exposing, particularly if it’s customer data whose privacy you’ve been entrusted to protect.

Running Your Own Model

The way to ameliorate the problems of accessing an LLM through an API is to either roll your own or run an open-source model in an environment that you control.

Building the kind of model that can compete with GPT-4 is really, really difficult, and it simply won’t be an option for any but the most elite engineering teams.

Using an open-source LLM, however, is a much more viable option. There are now many such models for text or code generation, and they can be fine-tuned for the specifics of your use case.

By and large, open-source models tend to be smaller and less performant than their closed-source cousins, so you’ll have to decide whether they’re good enough for you. And you should absolutely not underestimate the complexity of maintaining an open-sourced LLM. Though it’s nowhere near as hard as training one from scratch, maintaining an advanced piece of AI software is far from a trivial task.

All that having been said, this is one path you can take if you have the right applications in mind and the technical skills to pull it off.

How to Protect Brand Safety While Building Your Brand Persona

Throughout this piece, we’ve made mention of various ways in which LLMs can help supercharge your business while also warning of the potential damage a bad LLM response can do to your brand.

At present, there is no general-purpose way of making sure an LLM only does good things while never doing bad things. They can be startlingly creative, and with that power comes the possibility that they’ll be creative in ways you’d rather them not be (same as children, we suppose.)

Still, it is possible to put together an extensive testing suite that substantially reduces the possibility of a damaging incident. You need to feed the model many different kinds of interactions, including ones that are angry, annoyed, sarcastic, poorly spelled or formatted, etc., to see how it behaves.

What’s more, this testing needs to be ongoing. It’s not enough to run a test suite one weekend and declare the model fit for use, it needs to be periodically re-tested to ensure no bad behavior has emerged.

With these techniques, you should be able to build a persona as a company on the cutting edge while protecting yourself from incidents that damage your brand.

What Is the Future of LLMs and AI?

The business world moves fast, and if you’re not keeping up with the latest advances you run the risk of being left behind. At present, large language models like GPT-4 are setting the world ablaze with discussions of their potential to completely transform fields like customer experience chatbots.

If you want in on the action and you have the in-house engineering expertise, you could try to create your own offering. But if you would rather leverage the power of LLMs for chat-based applications by working with a world-class team that’s already done the hard engineering work, reach out to Quiq to schedule a demo.

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Semi-Supervised Learning Explained (With Examples)

Posted on The Quiq Team

From movie recommendations to chatbots as customer service reps, it seems like machine learning (ML) is absolutely everywhere. But one thing you may not realize is just how much data is required to train these advanced systems, and how much time and energy goes into formatting that data appropriately.

Machine learning engineers have developed many ways of trying to cut down on this bottleneck, and one of the techniques that have emerged from these efforts is semi-supervised learning.

Today, we’re going to discuss semi-supervised learning, how it works, and where it’s being applied.

What is Semi-Supervised Learning?

Semi-supervised learning (SSL) is an approach to machine learning (ML) that is appropriate for tasks where you have a large amount of data that you want to learn from, only a fraction of which is labeled.

Semi-supervised learning sits somewhere between supervised and unsupervised learning, and we’ll start by understanding these techniques because that will make it easier to grasp how semi-supervised learning works.

Supervised learning refers to any ML setup in which a model learns from labeled data. It’s called “supervised” because the model is effectively being trained by showing it many examples of the right answer.

Suppose you’re trying to build a neural network that can take a picture of different plant species and classify them. If you give it a picture of a rose it’ll output the “rose” label, if you give it a fern it’ll output the “fern” label, and so on.

The way to start training such a network is to assemble many labeled images of each kind of plant you’re interested in. You’ll need dozens or hundreds of such images, and they’ll each need to be labeled by a human.

Then, you’ll assemble these into a dataset and train your model on it. What the neural network will do is learn some kind of function that maps features in the image (the concentrations of different colors, say, or the shape of the stems and leaves) to a label (“rose”, “fern”.)

One drawback to this approach is that it can be slow and extremely expensive, both in funds and in time. You could probably put together a labeled dataset of a few hundred plant images in a weekend, but what if you’re training something more complex, where the stakes are higher? A model trained to spot breast cancer from a scan will need thousands of images, perhaps tens of thousands. And not just anyone can identify a cancerous lump, you’ll need a skilled human to look at the scan to label it “cancerous” and “non-cancerous.”

Unsupervised learning, by contrast, requires no such labeled data. Instead, an unsupervised machine learning algorithm is able to ingest data, analyze its underlying structure, and categorize data points according to this learned structure.

Semi-supervised learning

Okay, so what does this mean? A fairly common unsupervised learning task is clustering a corpus of documents thematically, and let’s say you want to do this with a bunch of different national anthems (hey, we’re not going to judge you for how you like to spend your afternoons!).

A good, basic algorithm for a task like this is the k-means algorithm, so-called because it will sort documents into k categories. K-means begins by randomly initializing k “centroids” (which you can think of as essentially being the center value for a given category), then moving these centroids around in an attempt to reduce the distance between the centroids and the values in the clusters.

This process will often involve a lot of fiddling. Since you don’t actually know the optimal number of clusters (remember that this is an unsupervised task), you might have to try several different values of k before you get results that are sensible.

To sort our national anthems into clusters you’ll have to first pre-process the text in various ways, then you’ll run it through the k-means clustering algorithm. Once that is done, you can start examining the clusters for themes. You might find that one cluster features words like “beauty”, “heart” and “mother”, another features words like “free” and “fight”, another features words like “guard” and “honor”, etc.

As with supervised learning, unsupervised learning has drawbacks. With a clustering task like the one just described, it might take a lot of work and multiple false starts to find a value of k that gives good results. And it’s not always obvious what the clusters actually mean. Sometimes there will be clear features that distinguish one cluster from another, but other times they won’t correspond to anything that’s easily interpretable from a human perspective.

Semi-supervised learning, by contrast, combines elements of both of these approaches. You start by training a model on the subset of your data that is labeled, then apply it to the larger unlabeled part of your data. In theory, this should simultaneously give you a powerful predictive model that is able to generalize to data it hasn’t seen before while saving you from the toil of creating thousands of your own labels.

How Does Semi-Supervised Learning Work?

We’ve covered a lot of ground, so let’s review. Two of the most common forms of machine learning are supervised learning and unsupervised learning. The former tends to require a lot of labeled data to produce a useful model, while the latter can soak up a lot of hours in tinkering and yield clusters that are hard to understand. By training a model on a labeled subset of data and then applying it to the unlabeled data, you can save yourself tremendous amounts of effort.

But what’s actually happening under the hood?

Three main variants of semi-supervised learning are self-training, co-training, and graph-based label propagation, and we’ll discuss each of these in turn.

Self-training

Self-training is the simplest kind of semi-supervised learning, and it works like this.

A small subset of your data will have labels while the rest won’t have any, so you’ll begin by using supervised learning to train a model on the labeled data. With this model, you’ll go over the unlabeled data to generate pseudo-labels, so-called because they are machine-generated and not human-generated.

Now, you have a new dataset; a fraction of it has human-generated labels while the rest contains machine-generated pseudo-labels, but all the data points now have some kind of label and a model can be trained on them.

Co-training

Co-training has the same basic flavor as self-training, but it has more moving parts. With co-training you’re going to train two models on the labeled data, each on a different set of features (in the literature these are called “views”.)

If we’re still working on that plant classifier from before, one model might be trained on the number of leaves or petals, while another might be trained on their color.

At any rate, now you have a pair of models trained on different views of the labeled data. These models will then generate pseudo-labels for all the unlabeled datasets. When one of the models is very confident in its pseudo-label (i.e., when the probability it assigns to its prediction is very high), that pseudo-label will be used to update the prediction of the other model, and vice versa.

Let’s say both models come to an image of a rose. The first model thinks it’s a rose with 95% probability, while the other thinks it’s a tulip with a 68% probability. Since the first model seems really sure of itself, its label is used to change the label on the other model.

Think of it like studying a complex subject with a friend. Sometimes a given topic will make more sense to you, and you’ll have to explain it to your friend. Other times they’ll have a better handle on it, and you’ll have to learn from them.

In the end, you’ll both have made each other stronger, and you’ll get more done together than you would’ve done alone. Co-training attempts to utilize the same basic dynamic with ML models.

Graph-based semi-supervised learning

Another way to apply labels to unlabeled data is by utilizing a graph data structure. A graph is a set of nodes (in graph theory we call them “vertices”) which are linked together through “edges.” The cities on a map would be vertices, and the highways linking them would be edges.

If you put your labeled and unlabeled data on a graph, you can propagate the labels throughout by counting the number of pathways from a given unlabeled node to the labeled nodes.

Imagine that we’ve got our fern and rose images in a graph, together with a bunch of other unlabeled plant images. We can choose one of those unlabeled nodes and count up how many ways we can reach all the “rose” nodes and all the “fern” nodes. If there are more paths to a rose node than a fern node, we classify the unlabeled node as a “rose”, and vice versa. This gives us a powerful alternative means by which to algorithmically generate labels for unlabeled data.

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Semi-Supervised Learning Examples

The amount of data in the world is increasing at a staggering rate, while the number of human-hours available for labeling it all is increasing at a much less impressive clip. This presents a problem because there’s no end to the places where we want to apply machine learning.

Semi-supervised learning presents a possible solution to this dilemma, and in the next few sections, we’ll describe semi-supervised learning examples in real life.

  • Identifying cases of fraud: In finance, semi-supervised learning can be used to train systems for identifying cases of fraud or extortion. Rather than hand-labeling thousands of individual instances, engineers can start with a few labeled examples and proceed with one of the semi-supervised learning approaches described above.
  • Classifying content on the web: The internet is a big place, and new websites are put up all the time. In order to serve useful search results it’s necessary to classify huge amounts of this web content, which can be done with semi-supervised learning.
  • Analyzing audio and images: This is perhaps the most popular use of semi-supervised learning. When audio files or image files are generated they’re often not labeled, which makes it difficult to use them for machine learning. Beginning with a small subset of human-labeled data, however, this problem can be overcome.

How Is Semi-Supervised Learning Different From…?

With all the different approaches to machine learning, it can be easy to confuse them. To make sure you fully understand semi-supervised learning, let’s take a moment to distinguish it from similar techniques.

Semi-Supervised Learning vs Self-Supervised Learning

With semi-supervised learning you’re training a model on a subset of labeled data and then using this model to process the unlabeled data. Self-supervised learning is different in that it’s showing an algorithm some fraction of the data (say the first 80 words in a paragraph) and then having it predict the remainder (the other 20 words in a paragraph.)

Self-supervised learning is how LLMs like GPT-4 are trained.

Semi-Supervised Learning vs Reinforcement Learning

One interesting subcategory of ML we haven’t discussed yet is reinforcement learning (RL). RL involves leveraging the mathematics of sequential decision theory (usually a Markov Decision Process) to train an agent to interact with its environment in a dynamic, open-ended way.

It bears little resemblance to semi-supervised learning, and the two should not be confused.

Semi-Supervised Learning vs Active Learning

Active learning is a type of semi-supervised learning. The big difference is that, with active learning, the algorithm will send its lowest-confidence pseudo-labels to a human for correction.

When Should You Use Semi-Supervised Learning?

Semi-supervised learning is a way of training ML models when you only have a small amount of labeled data. By training the model on just the labeled subset of data and using it in a clever way to label the rest, you can avoid the difficulty of having a human being label everything.

There are many situations in which semi-supervised learning can help you make use of more of your data. That’s why it has found widespread use in domains as diverse as document classification, fraud, and image identification.

So long as you’re considering ways of using advanced AI systems to take your business to the next level, check out our generative AI resource hub to go even deeper. This technology is changing everything, and if you don’t want to be left behind, set up a time to talk with us.

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Are Generative AI And Large Language Models The Same Thing?

Posted on J.R. Rettenmeyer

The release of ChatGPT was one of the first times an extremely powerful AI system was broadly available, and it has ignited a firestorm of controversy and conversation.

Proponents believe current and future AI tools will revolutionize productivity in almost every domain.

Skeptics wonder whether advanced systems like GPT-4 will even end up being all that useful.

And a third group believes they’re the first sparks of artificial general intelligence and could be as transformative for life on Earth as the emergence of homo sapiens.

Frankly, it’s enough to make a person’s head spin. One of the difficulties in making sense of this rapidly-evolving space is the fact that many terms, like “generative AI” and “large language models” (LLMs), are thrown around very casually.

In this piece, our goal is to disambiguate these two terms by discussing ​​the differences between generative AI vs. large language models. Whether you’re pondering deep questions about the nature of machine intelligence, or just trying to decide whether the time is right to use conversational AI in customer-facing applications, this context will help.

Let’s get going!

What Is Generative AI?

Of the two terms, “generative AI” is broader, referring to any machine learning model capable of dynamically creating output after it has been trained.

This ability to generate complex forms of output, like sonnets or code, is what distinguishes generative AI from linear regression, k-means clustering, or other types of machine learning.

Besides being much simpler, these models can only “generate” output in the sense that they can make a prediction on a new data point.

Once a linear regression model has been trained to predict test scores based on number of hours studied, for example, it can generate a new prediction when you feed it the hours a new student spent studying.

But you couldn’t use prompt engineering to have it help you brainstorm the way these two values are connected, which you can do with ChatGPT.

There are many types of generative AI, so let’s spend a few minutes discussing the major categories: image generation, music generation, code generation, and a few others.

How Is Generative AI Used To Make Images?

One of the first “wow” moments in generative AI came fairly recently when it was discovered that tools like Midjourney, DALL-E, and Stable Diffusion could create absolutely stunning images based on simple prompts like:

“Old man in a book store, ambient dappled sunlight, sedate, calm, close-up portrait.”

Depending on the wording you use, these images might be whimsical and futuristic, they might look like paintings from world-class artists, or they might look so photo-realistic you’d be convinced they’re about to start talking.

Created using DALL-E

Each of these tools is suited to specific applications. Midjourney seems to be best at capturing different artistic approaches and generating images that accurately capture an aesthetic. DALL-E tends to do better at depicting human figures, including faces and eyes. Stable Diffusion seems to do well at generating highly-detailed outputs, capturing subtleties like the way light reflects on a rain-soaked street.

(Note: these are all general impressions, it’s difficult to know how the tools will compare on any specific prompt.)

Broadly, this is known as “image synthesis”. And since we’re talking specifically about making images from text, this sub-domain is known as “text-to-image.”

A variant of this technique is text-to-video (alternatively: “text-to-4d”), which produces short clips or scenes based on text prompts. While text-to-video is still much more primitive than text-to-image, it will get better very quickly if recent progress in AI is any guide.

One interesting wrinkle in this story is that generative algorithms have generated something else along with images and animations: legal battles.

Earlier this year, Getty Images filed a lawsuit against the creators of Stable Diffusion, alleging that they trained their algorithm on millions of images from the Getty collection without getting permission first or compensating Getty in any way.

This has raised many profound questions about data rights, privacy, and how (or whether) people should be paid when their work is used to train a model that might eventually automate them out of a job.

We’re still in the early days of grappling with these issues, but they’re sure to make for fascinating case law in the years ahead.

How Is Generative AI Used To Make Music?

Given how successful advanced models have been in generating text (more on that shortly), it’s only natural to wonder whether similar models could also prove useful in generating music.

This is especially true because, on the surface, text and music share many obvious similarities (both are sequential, for example.) It would make sense, therefore, that the technical advances that have allowed coherent text production might also allow for coherent music production.

And they have! There are now a number of different tools, such as MusicLM, which are able to generate fairly high-quality audio tracks from prompts like:

“The main soundtrack of an arcade game. It is fast-paced and upbeat, with a catchy electric guitar riff. The music is repetitive and easy to remember, but with unexpected sounds, like cymbal crashes or drum rolls.”

As with using generative AI in images, creating artificial musical tracks in the style of popular artists has already sparked legal controversies. A particularly memorable example occurred just recently when a TikTok user supposedly created an AI-generated collaboration between Drake and The Weeknd, which then promptly went viral.

The track was removed from all major streaming services in response to backlash from artists and record labels, but it’s clear that ai music generators are going to change the way art is created in a major way.

How Is Generative AI Used For Coding?

It’s long been the dream of both programmers and non-programmers to simply be able to provide a computer with natural-language instructions (“build me a cool website”) and have the machine handle the rest. It would be hard to overstate the explosion in creativity and productivity this would initiate.

With the advent of code-generation models such as Replit’s Ghostwriter and GitHub Copilot, we’ve taken one more step towards that halcyon world.

As is the case with other generative models, code-generation tools are usually trained on massive amounts of data, after which point they’re able to take simple prompts and produce code from them.

You might ask it to write a function that converts between several different coordinate systems, create a web app that measures BMI, or translate from Python to Javascript.

As things stand now, the code is often incomplete in small ways. It might produce a function that takes an argument as input that is never used, for example, or which lacks a return function. Still, it is remarkable what has already been accomplished.

There are now software developers who are using models like ChatGPT all day long to automate substantial portions of their work, to understand new codebases with which they’re unfamiliar, or to write comments and unit tests.

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What Are Large Language Models?

Now that we’ve covered generative AI, let’s turn our attention to large language models (LLMs).

LLMs are a particular type of generative AI.

Unlike with MusicLM or DALL-E, LLMs are trained on textual data and then used to output new text, whether that be a sales email or an ongoing dialogue with a customer.

(A technical note: though people are mostly using GPT-4 for text generation, it is an example of a “multimodal” LLM because it has also been trained on images. According to OpenAI’s documentation, image input functionality is currently being tested, and is expected to roll out to the broader public soon.)

What Are Examples of Large Language Models?

By far the most well-known example of an LLM is OpenAI’s “GPT” series, the latest of which is GPT-4. The acronym “GPT” stands for “Generative Pre-Trained Transformer”, and it hints at many underlying details about the model.

GPT models are based on the transformer architecture, for example, and they are pre-trained on a huge corpus of textual data taken predominately from the internet.

GPT, however, is not the only example of an LLM.

The BigScience Large Open-science Open-access Multilingual Language Model – known more commonly by its mercifully-short nickname, “BLOOM” – was built by more than 1,000 AI researchers as an open-source alternative to GPT.

BLOOM is capable of generating text in almost 50 natural languages, and more than a dozen programming languages. Being open-sourced means that its code is freely available, and no doubt there will be many who experiment with it in the future.

In March, Google announced Bard, a generative language model built atop its Language Model for Dialogue Applications (LaMDA) transformer technology.

As with ChatGPT, Bard is able to work across a wide variety of different domains, offering help with planning baby showers, explaining scientific concepts to children, or helping you make lunch based on what you already have in your fridge.

How Are Large Language Models Trained?

A full discussion of how large language models are trained is beyond the scope of this piece, but it’s easy enough to get a high-level view of the process. In essence, an LLM like GPT-4 is fed a huge amount of textual data from the internet. It then samples this dataset and learns to predict what words will follow given what words it has already seen.

At first, its performance will be terrible, but over time it will learn that a sentence like “I sat down on the _____” probably ends with a word like “floor” or “chair”, and probably not a word like “cactus” (at least, we hope you’re not sitting down on a cactus!)

When a model has been trained for long enough on a large enough dataset, you get the remarkable performance seen with tools like ChatGPT.

Is ChatGPT A Large Language Model?

Speaking of ChatGPT, you might be wondering whether it’s a large language model. ChatGPT is a special-purpose application built on top of GPT-3, which is a large language model. GPT-3 was fine-tuned to be especially good at conversational dialogue, and the result is ChatGPT.

Are All Large Language Models Generative AI?

Yes. To the best of our knowledge, all existing large language models are generative AI. “Generative AI” is an umbrella term for algorithms that generate novel output, and the current set of models is built for that purpose.

Utilizing Generative AI In Your Business

Though truly powerful generative AI language models are less than a year old, they’re already being integrated into numerous business applications. Quiq Compose, for example, is able to study past interactions with customers to better tailor its future conversations to their particular needs.

From generating fake viral rap songs to generating photos that are hard to distinguish from real life, these powerful tools have already proven that they can dramatically speed up marketing, software development, and many other crucial business functions.

If you’re an enterprise wondering how you can use advanced AI technologies such as generative AI language models for applications like customer service, schedule a demo to see what the Quiq platform can offer you!

Prompt Engineering: What Is It—And How Can You Use It To Get The Most Out Of AI?

Posted on The Quiq Team

Think back to your school days. You come into class only to discover a timed writing assignment on the agenda. You have to respond to the provided prompt, quickly and accurately and will be graded against criteria like grammar, vocabulary, factual accuracy, and more.

Well, that’s what natural language processing (NLP) software like ChatGPT does daily. Except, when a computer steps into the classroom, it can’t raise its hand to ask questions.

That’s why it’s so important to provide AI with a prompt that’s clear and thorough enough to produce the best possible response.

What is ai prompt engineering?

A prompt can be a question, a phrase, or several paragraphs. The more specific the prompt is, the better the response.

Writing the perfect prompt — prompt engineering — is critical to ensure the NLP response is not only factually correct but crafted exactly as you intended to best deliver information to a specific target audience.

You can’t use low-quality ingredients in the kitchen to produce gourmet cuisine — and you can’t expect AI to, either.

Let’s revisit your old classroom again: did you ever have a teacher provide a prompt where you just weren’t really sure what the question was asking? So, you guessed a response based on the information provided, only to receive a low score.

In the post-exam review, the teacher explained what she was actually looking for and how the question was graded. You sat there thinking, “If I’d only had that information when I was given the prompt!”

Well, AI feels your pain.

The responses that NLP software provides are only as good as the input data. Learning how to communicate with AI to get it to generate desired responses is a science, and you can learn what works best through trial and error to continuously optimize your prompts.

Prompts that fail to deliver, and why.

What’s the root of the issue of prompt engineering gone wrong? It all comes down to incomplete, inconsistent, or incorrect data.

Even the most advanced AI using neural networks and deep learning techniques still needs to be fed the right information in the right way. When there is too little context provided, not enough examples, conflicting information from different sources, or major typos in the prompt, the AI can generate responses that are undesirable or just plain wrong.

How to craft the perfect prompt.

Here are some important factors to take into consideration for successful prompt engineering.

Clear instructions

Provide specific instructions and multiple examples to illustrate precisely what you want the AI to do. Words like “something,” “things,” “kind of,” and “it” (especially when there are multiple subjects within one sentence) can be indicators that your prompt is too vague.

Try to use descriptive nouns that refer to the subject of your sentence and avoid ambiguity.

  • Example (ambiguity): “She put the book on the desk; it was blue.”
  • What does “it” refer to in this sentence? Is the book blue, or is the desk blue?

Simple language

Use plain language, but avoid shorthand and slang. When in doubt, err on the side of overcommunicating and you can use trial and error to determine what shorthand approaches work for future, similar prompts. Avoid internal company or industry-specific jargon when possible, and be sure to clearly define any terms you may want to integrate.

Quality data

Give examples. Providing a single source of truth — for example, an article you want the AI to respond to questions about — will have a higher probability of returning factually correct responses based on the provided article.

On that note, teach the API how you want it to return responses when it doesn’t know the answer, such as “I don’t know,” “not enough information,” or simply “?”.

Otherwise, the AI may get creative and try to come up with an answer that sounds good but has no basis in reality.

Persona

Develop a persona for your responses. Should the response sound as though it’s being delivered by a subject matter expert or would it be better (legally or otherwise) if the response was written by someone who was only referring to subject matter experts (SMEs)?

  • Example (direct from SMEs): “Our team of specialists…”
  • Example (referring to SMEs): “Based on recent research by experts in the field…”

Voice, style, and tone

Decide how you want to represent your brand’s voice, which will largely be determined by your target audience. Would your customer be more likely to trust information that sounds like it was provided by an academic, or would a colloquial voice be more relatable?

Do you want a matter-of-fact, encyclopedia-type response, a friendly or supportive empathetic approach, or is your brand’s style more quick-witted and edgy?

With the right prompt, AI can capture all that and more.

Quiq takes prompt engineering out of the equation.

Prompt engineering is no easy task. There are many nuances to language that can trick even the most advanced NLP software.

Not only are incorrect AI responses a pain to identify and troubleshoot, but they can also hurt your business’s reputation if they aren’t caught before your content goes public.

On the other hand, manual tasks that could be automated with NLP waste time and money that could be allocated to higher-priority initiatives.

Quiq uses large language models (LLMs) to continuously optimize AI responses to your company’s unique data. With Quiq’s world-class Conversational AI platform, you can reduce the burden on your support team, lower costs, and boost customer satisfaction.

Contact Quiq today to see how our innovative LLM-built features improve business outcomes.

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The Rise of Conversational AI: Why Businesses Are Embracing It

Posted on The Quiq Team

Movies may have twisted our expectations of artificial intelligence—either giving us extremely high expectations or making us think it’s ready to wipe out humanity.

But the reality isn’t on those levels. In fact, you’re already using AI in your daily life—but it’s so ingrained in your technology you probably don’t even notice. Netflix and Spotify both use AI to personalize your content recommendations. Siri, Alexa, and Google Assistant use it as well.

Conversational AI, like what Quiq uses to power our chatbots, takes artificial intelligence to the next level. See what it is and how you can use it in your business.

What is conversational AI?

Conversational artificial intelligence (AI) is a collection of technologies that create a human-like experience. It combines natural language processing (NLP), machine learning, and other technologies to enhance streamlined conversations. This can be used in many applications, like chatbots and voice (like Siri and Alexa). The most common use case for conversational AI in the business-to-customer world is through an AI chatbot messaging experience.

Unlike rule-based chatbots, those powered by conversational AI generate responses and adapt to user behavior over time. Rule-based chatbots were also limited to what you put in them—meaning if someone phrased a question differently than you wrote it (or used slang/colloquialisms/etc.), it wouldn’t understand the question. Conversational AI can also help chatbots understand more complex questions.

Putting technical terms in context.

Companies throw around a lot of technical terms when it comes to artificial intelligence, so here are what they mean and how they’re used to improve your business.

Rules-based chatbots: Earlier chatbot iterations (and some current low-cost versions) work mainly through pre-defined rules. Your business (or service provider) writes specific guidelines for the chatbot to follow. For example, when a customer says “Hi,” the chatbot responds, “Hello, how may I help you?”

Another example is when a customer asks about a return. The chatbot is programmed to give a specific response, like, “Here’s a link to the return policy.”

However, the problem with rule-based chatbots is that they can be limiting. It only knows how to handle situations based on the information programmed into it. So if someone says, “I don’t like this product, what can I do?” and you haven’t planned for that question, the chatbot won’t have a response.

Machine learning: Machine learning is a way to combat the problem posed above. Instead of giving the chatbot specific parameters complete with pre-written questions and answers, machine learning helps chatbots make decisions based on the information provided.

Machine learning helps chatbots adapt over time based on customer conversations. Instead of giving the bot specific ways to answer specific questions, you show it the basic rules, and it crafts its own response. Plus, since it means your chatbot is always learning, it gets better the longer you use it.

Natural language processing: As humans and speakers of the English language, we know that there are different ways to ask every question. For example, a customer who wants to know when an item is back in stock may ask, “When is X back in stock?” or they might say, “When will you get X back in?” or even, “When are you restocking X?” Those three questions all mean the same thing, and as humans, we naturally understand that. But a rules-based bot must be told that those mean the same things, or they might not understand it.

Natural language processing (NLP) uses AI technology to help chatbots understand that those questions are all asking the same thing. It also can determine what information it needs to answer your question, like color, size, etc.

NLP also helps chatbots answer questions in a more human-like way. If you want your chatbot to sound more human (and you should), then find one that uses NLP.

Web-based SDK: A web-based SDK (that’s a software development kit for non-developers) is a set of tools and resources developers use to integrate programs (in this case, chatbots) into websites and web-based applications.

What does this mean for your chatbot? Context. When a user says, “I need help with my order,” the chatbot can use NLP to identify “help” and “order.” Then it can look back at previous conversations, pull the customers’ order history, and more—if the data is there.

Contextual conversations are everything in customer service—so this is a big factor in building a successful chatbot using conversational AI. In fact, 70% of customers expect anyone they’re speaking with to have the full context. With a web-based SDK, your chatbot can do that too.

The benefits of conversational AI.

Using chatbots with conversational AI provides benefits across your business, but the clearest wins are in your contact center. Here are three ways chatbots improve your customer service.

24/7 customer support.

Your customer service agents need to sleep, but your conversational AI chatbot doesn’t. A chatbot can answer questions and contain customer issues while your contact center is closed. Any issues they can’t solve, they can pass along to your agents the next day. Not only does that give your customers 24/7 service, but your agents will have less of a backlog when they return to work.

Faster response times.

When your agents are inundated with customers, an AI chatbot can pick up the slack. Send your chatbot in to greet customers immediately, let them know the wait time, or even start collecting information so your agents can get to the root of the problem faster. Chatbots powered with AI can also answer questions and solve easy customer issues, skipping human agents altogether.

For more ways AI chatbots can improve your customer service, read this >

More present customer service agents.

Chatbots can handle low-level customer queries and give agents the time and space to handle more complex issues. Not only will this result in better customer service, but agents will be happier and less stressed overall.

Plus, chatbots can scale during your busy seasons. You’ll save on costs since you won’t have to hire more agents, and the agents you have won’t be overworked.

How to make the most of AI technology.

Unfortunately, you can’t just plug and play with conversational AI and expect to become an AI company. Just like any other technology, it takes prep work and thoughtful implementation to get it right—plus lots of iterations.

Use these tips to make the most of AI technology:

Decide on your AI goals.

How are you planning on using conversational AI? Will it be for marketing? Customer service? All of the above? Think about what your main goals are and use that information to select the right AI partner.

Choose the right conversational AI platform.

Once you’ve decided on how you want to use conversational AI, select the right partner to help you get there. Think about aspects like ease of use, customization, scalability, and budget.

Design your chatbot interactions.

Even with artificial intelligence, you still have to put the work in upfront. What you do and how you do it will vary greatly depending on which platform you go with. Design your chatbot conversations with these things in mind:

  • Your brand voice
  • Personalization
  • Customer service best practices
  • Logical conversation flows
  • Concise messages

Build a partnership between agents and chatbots.

Don’t launch the chatbot independently of your customer service agents. Include them in the training and launch, and start to build a working relationship between the two. Agents and chatbots can work together on customer issues, both popping in and out of the conversation seamlessly. For example, a chatbot can collect information from the customer upfront and pass it to the agent to solve the issue. Then, when the agent is done, they can bring the chatbot back in to deliver a customer survey.

Test and refine.

Sometimes, you don’t know what you don’t know until it happens. Test your chatbot before it launches, but don’t stop there. Keep refining your conversations even after you’ve launched.

What does the future hold for conversational AI?

There are many exciting things happening in AI right now, and we’re only on the cusp of delving into what it can really do.

The big prediction? For now, conversational AI will keep getting better at what it’s already doing. More human-like interactions, better problem-solving, and more in-depth analysis.

In fact, 75% of customers believe AI will become more natural and human-like over time. Gartner is also predicting big things for conversational AI, saying by 2026, conversational AI deployments within contact centers will reduce agent labor costs by $80 billion.

Why should you jump in now when bigger things are coming? It’s simple. You’ll learn to master conversational AI tools ahead of your competitors and earn an early competitive advantage.

How Quiq does conversational AI.

To ensure you give your customers the best experience, Quiq powers our entire platform with conversational AI. Here are a few stand-out ways Quiq uniquely improves your customer service with conversational AI.

Design customized chatbot conversations.

Create chatbot conversations so smooth and intuitive that it feels like you’re talking to a real person. Using the best conversational AI techniques, Quiq’s chatbot gives customers quick and intelligent responses for an up-leveled customer experience.

Help your agents respond to customers faster.

Make your agents more efficient with Quiq Compose. Quiq Compose uses conversational AI to suggest responses to customer questions. How? It uses information from similar conversations in the past to craft the best response.

Empower agent performance.

Tools like our Adaptive Response Timer (ADT) prioritizes conversations based on how fast or slow customers respond. The conversational AI platform also uses AI to analyze customer sentiment to give extra attention to customers who need it.

This is just the beginning.

This is just a taste of what conversational AI can do. See how Quiq can apply the latest technology to your contact center to help you deliver exceptional customer service.

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Customer Service in the Travel Industry: How to Do More with Less

Posted on The Quiq Team

Doing more with less is nothing new for the travel industry. It’s been tough out there for the last few years—and while the future is bright, travel and tourism businesses are still facing a labor shortage that’s causing customer satisfaction to plummet.

While HR leaders are facing the labor shortage head-on with recruiting tactics and budget increases, customer service teams need to search for ways to provide the service the industry is known for without the extra body count.

In other words… You need to do more with less.

The best way to do that is with a conversational AI platform. Whether a hotel, airline, car rental company or experience provider, you can provide superior service to your customers without overworking your support team.

Keep reading to take a look at the state of the travel industry’s labor shortage and how you can still provide exceptional customer service.

Travel is back, but labor is not.

In 2019, the travel and tourism industry accounted for 1 in 10 jobs around the world. Then the pandemic happened, and the industry lost 62 million jobs overnight, according to the World Travel & Tourism Council (WTTC).

Now that most travel restrictions, capacity limits, and safety restrictions are lifted, much of the world is ready to travel again. The pent-up demand has caused the tourism and travel industry to outpace overall economic growth. In 2021, the GDP grew by 21.7%, while the overall economy only grew by 5.8%, according to the WTTC.

In 2021, travel added 18.2 million jobs globally, making it difficult to keep up with labor demands. In the U.S., 1 in 9 jobs went unfilled in 2021.

What’s causing the shortage? A combination of factors:

  • Flexibility: Over the last few years, there has been a mindset shift when it comes to work-life balance. Many people aren’t willing to give up weekends and holidays with their families to work in hospitality.
  • Safety: Many jobs in hospitality work on the frontline, interacting with the public on a regular basis. Even though the pandemic has cooled in most parts of the world, some workers are still hesitant to work face-to-face. This goes double for older workers and those with health concerns, who may have either switched industries or dropped out of the workforce altogether.
  • Remote work: The pandemic made remote work more feasible for many industries, and travel requires a lot of in-person work and interactions.

How is the labor shortage impacting customer service?

As much as we try to separate those shortages from affecting service, customers feel it. According to the American Customer Satisfaction Index, hotel guests were 2.7% less satisfied overall between 2021 and 2022. Airlines and car rental companies also dropped 1.3% each.

While there are likely multiple reasons factoring into lower customer satisfaction rates, there’s no denying that the labor shortage has an impact.

As travel ramps back up, there’s an opportunity to reshape the industry at a fundamental level. The world is ready to travel again, but demand is outpacing your ability to grow. While HR is hard at work recruiting new team members, it’s time to look at your operations and see what you can do to deliver great customer service without adding to your staff.

What a conversational AI platform can do in the travel industry.

First, what is conversational AI? Conversational AI combines multiple technologies (like machine learning and natural language processing) to enable human-like interactions between people and computers. For your customer service team, this means there’s a coworker that never sleeps, never argues, and seems to have all the answers.

A conversational AI platform like Quiq can help support your travel business’s customer service team with tools designed to speed conversations and improve your brand experience.

In short, a conversational AI platform can help businesses in the travel industry provide excellent customer service despite the current labor shortage. Here’s how.

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Resolve issues faster with conversational AI support.

When you’re short-staffed, you can’t afford inefficient customer conversations. Switching from voice-based customer service to messaging comes with its own set of benefits.

Using natural language processing (NLP), a conversational AI platform can identify customer intent based on their actions or conversational cues. For example, if a customer is stuck on the booking page, maybe they have a question about the cancellation policy. By starting with some basic customer knowledge, chatbots or human agents can go into the conversation with context and get to the root of the problem faster.

Conversational AI platforms can also route conversations to the right agent, so agents spend less time gathering information and more time solving the problem. Plus, messaging’s asynchronous nature means customer service representatives can handle 6–8 conversations at once instead of working one-on-one. But conversational AI for customer service provides even more opportunities for speed.

Anytime access to your customer service team.

Many times, workers leaving the travel industry cite a lack of schedule flexibility as one of their reasons for leaving. Customer service doesn’t stop at 5 o’clock, and support agents end up working odd hours like weekends and holidays. Plus, when you’re short-staffed, it’s harder to cover shifts outside of normal business hours.

Chatbots can help provide customer service 24/7. If you don’t already provide anytime customer service support, you can use chatbots to answer simple questions and route the more complex questions to a live agent to handle the next day. Or, if you already have staff working evening shifts, you can use chatbots to support them. You’ll require fewer human agents during off times while your chatbot can pick up the slack.

Connect with customers in any language.

Five-star experiences start with understanding. You’re in the travel business, so it’s not unlikely that you’ll encounter people who speak different languages. When you’re short-staffed, it’s hard to ensure you have enough multilingual support agents to accommodate your customers.

Conversational AI platforms like Quiq offer translation capabilities. Customers can get the help they need in their native language—even if you don’t have a translator on staff.

Work-from-anywhere capabilities.

One of the labor shortage’s root causes is the move to remote work. Many customer-facing jobs require working in person. That limits your labor pool to people within the immediate area. The high cost of living in cities with increased tourism can push locals out.

Moving to a remote-capable conversational tool will expand your applicant pool outside your immediate area. You can attract a wider range of talented customer service agents to help you fill open positions.

Build automation to anticipate customer needs.

A great way to reduce the strain on a short-staffed customer service team? Prevent problems before they happen.

A lot of customer service inquiries are simple, routine questions that agents have to answer every day. Questions about cancellation policies, cleaning and safety measures, or special requests happen often—and can all be handled using automation.

Use conversational AI to set up personalized messages based on behavioral or timed triggers. Here are a few examples:

  • When customers book a vacation: Automatically send a confirmation text message with their booking information, cancellation policy, and check-in procedures.
  • The day before check-in: Send a reminder with check-in procedures, along with an option for any special requests.
  • During their vacation: Offer up excursion ideas, local restaurant reservations, and more. You can even book the reservation or complete the transaction right within the messaging platform.
  • After an excursion: Send a survey to collect feedback and give customers an outlet for their positive or negative feedback.

By anticipating these customer needs, your agents won’t have to spend as much time fielding simple questions. And the easy ones that do come in can be handled by your chatbot, leaving only more complex issues for your smaller team.

Don’t let a short staff take away from your customer service.

There are few opportunities to make something both cheaper and better. Quiq is one of them. Quiq’s conversational AI Platform isn’t just a stop-gap solution while the labor market catches up with the travel industry’s needs. It will actually improve your customer service experience while helping you do more with less.

Using AI to Streamline Messaging

Posted on The Quiq Team

Conversational AI typically refers to leveraging bots to satisfy your customers while scaling your contact center.

At Quiq, we love bots, but we also take a broader view of Conversational AI. After all, bots are only part of digital CX.

In our view, Conversational AI also means helping your live agents work more efficiently and streamlining your operations.

In this article, we’re going to focus on how AI can (and should) be used to manage the nuances of messaging as part of a broader suite of Conversational AI.

The Need for Conversational AI

In The Nature Of Messaging, we described how messaging is a unique channel. It fluctuates between synchronous and asynchronous communication styles. It’s informal. Live agents can work on multiple messaging conversations concurrently.

All of this implies a system that can:

1. Track and prioritize the conversations assigned to a live agent.

In order to prioritize, we must understand who is expected to respond next.

2. Manage the agent’s workload.

  • Keep them busy, but not too busy.
  • Prioritize customers who are actively engaged.
  • Move inactive customers out of the way, without losing their session.

3. Map free-flowing streams of messages into tickets in traditional CRM systems.

In order to achieve the above, you need a purpose-built system (like Quiq) that handles the fluctuating synchronicity of conversations amidst the backdrop of agent concurrency. The system is also going to need a hefty dose of AI to do the best possible job.

Let’s consider some examples to explore why.

Here’s a pretty typical inbound service conversation that was routed directly to a live agent.

The agent sent the last message, but is it the customer’s turn to respond?

No.

The agent essentially promised a follow-up. The system should still prioritize this conversation.

The traditional algorithm employed in email management systems is that the two parties should take turns, but that doesn’t work in conversational settings because messages are shorter and less formal.

We need NLP/AI here.

Here we have the opposite situation. The system shouldn’t prioritize this conversation or set any sort of SLA timer because the burden of response is on the customer.

If the customer fails to follow up within a reasonable timeframe (10 minutes?), the system should move this conversation to an inactive state to make room for customers who are more engaged.

Do you remember choose your own adventure books? In this example, you get to pick what happens next:

  1. Nothing. The conversation is over.
  2. An hour passes. The customer responds with “You too!”
  3. An hour passes. The customer responds with “Actually, I don’t want green after all.”
  4. An hour passes. The customer responds with “I have a different question for you.”

Compared to phone and email, it’s less clear when a messaging conversation is actually over.

Obviously, we don’t want to just leave the conversation open; that delays helping other customers.

So the system should automatically inactivate and/or close it.

If scenario 2 happens, what should we do?

We definitely don’t want to open another tracking ticket, and we may not even want to reopen the conversation and route it to the agent (especially if that agent isn’t online anymore). We call this the ‘long goodbye’ problem, or more generally, an unimportant response.

If scenario 3 happens, we need to reopen the conversation and it should be associated with the same ticket in the CRM and ideally routed to the same agent.

If scenario 4 happens, we should start a new conversation associated with a new ticket and route the conversation to our entry point (e.g. a bot) rather than directly routing to the agent.

In messaging apps, there isn’t a clear start and end point to a conversation—and there isn’t an equivalent of an email ‘Subject’.

It’s just a stream of messages with potentially long delays between them. So in order to solve the above problems, we need a deep understanding of the message content.

The Impact

The examples we explored above aren’t just academic. They’re impactful to your operations.

Consider the following stats taken from across our user base (your org’s exact numbers might differ):

10% of conversations will have a late, ‘unimportant’ message arrive.

  • Failure to recognize these as continuations of the previous conversation causes superfluous records that impact analytics.
  • Agents are unnecessarily distracted.

The traditional ‘take turns’ response algorithm is wrong 30% of the time in messaging.

  • If we fail to prioritize a conversation where the customer is expecting a response, we risk missing SLAs and angering customers, while forcing agents to attempt their own prioritization.
  • If we prioritize a conversation that is actually waiting on the customer, we decrease efficiency by distracting the agent and delaying service to other customers.

20% of your messaging conversations will reopen in a 72-hour period.

It’s important to recognize when an important message arrives and determine if it represents a new topic or a continuation.

Our Approach

At Quiq, our goal is to leverage AI to have a positive and immediate impact on our customers and their businesses.

We follow the latest research and pragmatically adapt and apply AI to the context of conversational business messaging.

For the majority of our AI modeling tasks, it’s not sufficient to simply consider the text of a single message in order to make a decision.

Instead, we must consider all of the recent transcripts, including the sequence of individual messages and their authors. This deep understanding of the conversation transcript enables us to achieve high accuracy on problems like the ones presented in this article.

Stay tuned as we build out more!