Quiq

Month: April 2023

LLM vs Generative AI vs Agentic AI: What’s the Difference?

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,”  “large language models” (LLMs) and now “agentic AI” are thrown around very casually.

In this piece, our goal is to disambiguate these three terms by discussing ​​the differences between generative AI, large language models, and agentic AI. 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.

What Is Generative AI?

Of the three 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 key features of generative AI, so let’s spend a few minutes discussing how it can be used and the benefits it can provide.

Key Features of Generative AI

Generative AI is designed to create new content, learning from vast datasets to produce text, images, audio, and video. Its capabilities extend beyond simple data processing, making it a powerful tool for creativity, automation, and personalization. 

Content Generation

At its core, Generative AI excels at producing unique and original content across multiple formats, including text, images, audio, and video. Unlike traditional AI systems that rely on predefined rules, generative models leverage deep learning to generate coherent and contextually relevant outputs. This creative capability has revolutionized industries ranging from marketing to entertainment.

Data-Driven Learning

Generative AI models are trained on vast datasets, allowing them to learn complex patterns and relationships within the data. These models use deep neural networks, particularly transformer-based architectures, to process and generate information in a way that mimics human cognition. By continuously analyzing new data, generative AI can refine its outputs and improve over time, making it increasingly reliable for content generation, automation, and decision-making.

Adaptability & Versatility

One of the most powerful aspects of Generative AI is its ability to function across diverse industries and use cases. Whether it’s generating realistic human-like conversations in chatbots, composing music, or designing virtual environments, the technology adapts seamlessly to different applications. Its versatility allows businesses to leverage AI-driven creativity without being limited to a single domain.

Customization & Personalization

Generative AI can tailor its outputs based on user inputs, preferences, or specific guidelines. This makes it an invaluable tool for personalized content creation, such as crafting targeted marketing messages, customizing chatbot responses, or even generating personalized artwork. By adjusting parameters or fine-tuning models with proprietary data, businesses can ensure that the AI-generated content aligns with their brand voice and audience expectations.

Effeciency & Automation

Beyond creativity, Generative AI significantly enhances efficiency by automating tasks that traditionally require human effort. Whether it’s generating reports, summarizing large volumes of text, or producing high-quality design assets, AI-driven automation saves time and resources. This efficiency allows businesses to scale their operations while reducing costs and freeing up human talent for higher-level strategic work.

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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

Key Features of Large Language Models

LLMs represent a breakthrough in AI-powered language processing, offering unparalleled natural language capabilities, scalability, and adaptability. Their ability to understand and generate text with contextual awareness makes them invaluable across industries. Below, we explore the key features that make LLMs so powerful and their significance in real-world applications.

Natural Language Understanding & Generation

One of the defining characteristics of LLMs is their ability to comprehend and generate human language with contextually relevant and coherent output. Unlike traditional rule-based NLP systems, LLMs leverage deep learning to process vast amounts of text, enabling them to recognize nuances, idioms, and contextual dependencies.

Why this matters: This enables more natural interactions in chatbots, virtual assistants, and customer support tools. It also improves content generation for marketing, reporting, and creative writing, while multilingual capabilities enhance accessibility and global communication.

Scalability & Versatility:

LLMs are designed to process and generate text at an unprecedented scale, making them versatile across a wide range of applications. They can analyze large datasets, respond to queries in real-time, and generate text in multiple formats—from technical documentation to creative storytelling.

Why this matters: Their scalability allows businesses to automate tasks, improve decision-making, and generate personalized content efficiently. This versatility makes them useful across industries like healthcare, finance, and education, streamlining operations and enhancing user engagement.

Adaptability Through Fine-Tuning

While general-purpose LLMs are highly capable, their performance can be further enhanced through fine-tuning—a process that tailors the model to specific domains or tasks. By training an LLM on industry-specific data, organizations can improve accuracy, reduce bias, and align responses with their unique needs.

Why this matters: Fine-tuning increases accuracy for specialized tasks, ensuring better performance in industries like healthcare and law. It also helps businesses maintain brand consistency and reduces the need for manual corrections, leading to more efficient workflows.

What is Agentic AI?

Agentic AI refers to artificial intelligence systems that go beyond passive data processing to actively pursue objectives with minimal human intervention. Unlike traditional AI models that rely on explicit prompts or predefined workflows, agentic AI autonomously takes initiative, gathers information, and makes decisions in pursuit of a goal. 

At its core, agentic AI operates with a level of autonomy that allows it to dynamically adapt to new information, refine its approach, and execute tasks with greater independence. These systems can analyze complex scenarios, break down multi-step problems, and determine the best course of action without requiring constant human oversight.

Advancements in AI, from reinforcement learning to multi-agent collaboration, have enabled agentic AI to evolve from passive tools into autonomous problem-solvers. Businesses now use it to streamline workflows, enhance decision-making, and drive efficiency, signaling a shift toward proactive AI systems.

What are some of the key features of Agentic AI?

As stated before, Agentic AI represents a significant evolution beyond traditional AI models, offering enhanced autonomy and decision-making capabilities. Let’s discuss some of Agentic AI’s key features:

Autonomous Action

One of the defining characteristics of Agentic AI is its ability to operate without constant human intervention. Rather than waiting for step-by-step instructions, it executes tasks independently, identifying the necessary actions to reach an objective. This autonomy allows it to function in dynamic environments, where manual oversight would be inefficient or impractical.

Dynamic Decision Making

Agentic AI leverages real-time data to continuously refine its decision-making process. It evaluates multiple factors, adapts to changing conditions, and optimizes its approach based on the latest available information. This ability to course-correct and adjust strategies in real-time makes it particularly effective for complex problem-solving and unpredictable scenarios.

Goal-Oriented Behavior

Unlike conventional AI models that react to prompts, Agentic AI operates with a clear end goal in mind. It identifies obstacles, prioritizes tasks, and makes trade-offs to achieve its objectives efficiently. Whether optimizing workflows, automating multi-step processes, or navigating constraints, it maintains a results-driven approach.

Proactive Resource Gathering

To function effectively, Agentic AI does not simply wait for relevant data or tools to be provided—it actively seeks out the necessary resources. This can include retrieving information from databases, leveraging APIs, integrating with other systems, or even initiating sub-tasks to support the primary goal. This proactive approach enhances efficiency and reduces dependency on human input.

Self-Improvement Through Feedback

Agentic AI continuously refines its performance through iterative learning. By analyzing the outcomes of past actions, it identifies areas for improvement and adjusts future behaviors accordingly. This feedback loop allows it to become more effective over time, reducing errors and increasing efficiency in completing assigned tasks.

What Are Some Examples of Agentic AI?

Now that we have explained what Agentic AI is and some of its key features, you may be wondering how businesses in various industries are using Agentic AI. Here are a few examples:

1. Personalized AI Assistants: Beyond Basic Task Execution

AI assistants have come a long way from setting reminders and answering basic questions. Today’s agentic AI assistants can handle entire workflows, making life a whole lot easier.

Imagine having an AI-powered executive assistant that not only manages your calendar but also rearranges meetings when scheduling conflicts pop up, prioritizes your emails, and even drafts responses for you. In sales, AI agents integrated into CRMs can track conversations, spot promising leads, and automatically schedule follow-ups—no manual input required.

2. AI in Healthcare: Keeping an Eye on Your Health

Healthcare is another area where agentic AI is making a real difference. Instead of passively analyzing data, these AI systems can continuously monitor patient health, detect problems early, and even adjust treatment plans on the fly.

For example, some AI-powered health monitoring tools track vital signs in real-time, alerting doctors if something seems off. Others can analyze medical records and suggest personalized treatments based on a patient’s history. In some cases, AI can even adjust medication dosages automatically, ensuring patients get the right treatment without constant doctor intervention.

3. AI That Actually Solves Customer Support Issues

We’ve all had frustrating experiences with chatbots that don’t understand what we’re asking. Agentic AI is fixing that by powering virtual support agents that don’t just respond to questions—they solve problems.

Picture this: You need to return an item, and instead of navigating through endless menus, an AI agent processes your return, updates your order, and even schedules a pickup without you lifting a finger. In IT support, AI-powered agents can troubleshoot issues, restart systems, and even execute fixes automatically. No more waiting on hold for help—AI’s got it covered.

How Do Agentic AI,  Generative AI, and LLM’s Compare?

Artificial intelligence has rapidly evolved, with distinct categories emerging to define different capabilities and use cases. While Generative AI, Large Language Models (LLMs), and Agentic AI share foundational principles, they each serve unique purposes.

Key Differences Between Generative AI, LLMs, and Agentic AI

  1. Generative AI: This is the broad umbrella term for AI models that create content, whether text, images, music, or video. These models generate outputs based on patterns learned from large datasets but typically require user input to function effectively.
  2. Large Language Models: A subset of Generative AI, LLMs specialize in language-based tasks such as text generation, summarization, translation, and answering questions. They process vast amounts of textual data to produce human-like responses but do not inherently make decisions or take autonomous action.
  3. Agentic AI: Unlike Generative AI and LLMs, Agentic AI goes a step further by incorporating autonomy and goal-driven behavior. It not only generates outputs but also plans, executes, and adapts actions based on objectives. This makes Agentic AI well-suited for tasks that require decision-making, iterative problem-solving, and multi-step execution.

How These AI Systems Can Work Together

Agentic AI, Generative AI, and LLMs are not mutually exclusive; rather, they complement each other in complex workflows. For example:

  • A Generative AI model might generate a marketing email.
  • An LLM could refine the email’s tone and structure based on customer preferences.
  • An Agentic AI system could autonomously schedule and send the email, analyze customer responses, and iterate on the next campaign.

This synergy enables businesses and organizations to streamline operations, automate complex workflows, and improve decision-making at scale.

When to Use Generative AI, LLMs, or Agentic AI

As AI continues to evolve, different types of AI serve distinct roles in automation, content creation, and decision-making. Choosing the right approach—Generative AI, Large Language Models (LLMs), or Agentic AI—depends on the complexity of the task, the level of autonomy required, and the desired outcome. Here’s when to use each.

When to Use Generative AI

Generative AI is best suited for tasks that involve creativity, personalization, and idea generation. It excels at producing original content and enhancing user engagement by tailoring outputs dynamically.

  1. For Creative Content Generation: Generative AI shines when creating unique visuals, music, text, or videos. It’s widely used in industries like marketing, design, and entertainment.
  2. For Prototyping and Idea Generation: When brainstorming ideas or rapidly iterating on design concepts, generative AI can provide inspiration and streamline workflows.
  3. For Enhancing Personalization: Generative AI helps tailor content for individual users, making it a powerful tool in marketing, product recommendations, and customer engagement.

When to Use Large Language Models (LLMs)

LLMs specialize in processing and generating human-like text, making them ideal for knowledge work, communication, and conversational AI.

  1. For Text-Based Tasks: LLMs handle content creation, summarization, translation, and text analysis with high efficiency.
  2. For Conversational AI: They power chatbots, virtual assistants, and customer support tools by enabling natural, context-aware conversations.
  3. For Knowledge Work and Research: LLMs assist in research, code generation, and complex problem-solving, making them valuable for technical fields.

When to Use Agentic AI

Agentic AI goes beyond content generation and text processing—it autonomously executes tasks, makes decisions, and manages workflows with minimal human input.

  1. For Automating Multi-Step Tasks: Agentic AI can plan, make decisions, and execute complex workflows without constant human oversight.
  2. For Goal-Oriented, CX-Focused Systems: In scenarios where AI needs to take action toward a specific objective, agentic AI ensures execution beyond just responding to queries.
  3. For Enhancing Productivity in Complex Workflows: When managing multiple tools or systems, agentic AI improves efficiency by handling strategic yet repetitive tasks.

Utilizing Generative AI In Your Business

AI is evolving fast, but not all AI is created equal. Generative AI is great for creativity, LLMs handle text-based tasks, but agentic AI is the game-changer—turning AI from an assistant into an autonomous problem-solver. That’s where Quiq stands out. Instead of just generating responses, Quiq’s agentic AI takes action, automating complex tasks and making real decisions so businesses can scale without the bottlenecks. It’s AI that doesn’t just assist—it gets things done.

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

A Deep Dive on Large Language Models—And What They Mean For You

Posted on The Quiq Team

The release of OpenAI’s ChatGPT in late 2022 has utterly transformed the conversation around artificial intelligence. Whether it’s generating functioning web apps with just a few prompts, writing Spanish-language children’s stories about the blockchain in the style of Dr. Suess, or opining on the virtues and vices of major political figures, its ability to generate long strings of coherent, grammatically-correct text is shocking.

Seen in this light, it’s perhaps no surprise that ChatGPT has achieved such a staggering rate of growth. The application garnered a million users less than a week after its launch.

It’s believed that by January of 2023, this figure had climbed to 100 million monthly users, blowing past the adoption rates of TikTok (which needed nine months to get to this many monthly users) and Instagram (which took over two years.)

Naturally, many have become curious about the “large language model” (LLM) technology that makes ChatGPT and similar kinds of disruptive generative AI possible.

In this piece, we’re going to do a deep dive on LLMs, exploring how they’re trained, how they work internally, and how they might be deployed in your business. Our hope is that this will arm Quiq’s customers with the context they need to keep up with the ongoing AI revolution.

What Are Large Language Models?

LLMs are pieces of software with the ability to interact with and generate a wide variety of text. In this discussion, “text” is used very broadly to include not just existing natural language but also computer code.

A good way to begin exploring this subject is to analyze each of the terms in “large language model”, so let’s do that now. Here’s our large language models overview:

LLMs Are Models.

In machine learning (ML), you can think of a model as being a function that maps inputs to outputs. Early in their education, for example, machine learning engineers usually figure out how to fit a linear regression model that does something like predict the final price of a house based on its square footage.

They’ll feed their model a bunch of data points that look like this:

House 1: 800 square feet, $120,000
House 2: 1000 square feet, $175,000
House 3: 1500 square feet, $225,000

And the model learns the relationship between square footage and price well enough to roughly predict the price of homes that weren’t in its training data.

We’ll have a lot more to say about how LLMs are trained in the next section. For now, just be aware that when you get down to it, LLMs are inconceivably vast functions that take the input you feed them and generate a corresponding output.

LLMs Are Large.

Speaking of vastness, LLMs are truly gigantic. As with terms like “big data”, there isn’t an exact, agreed-upon point at which a basic language model becomes a large language model. Still, they’re plenty big enough to deserve the extra “L” at the beginning of their name.

There are a few ways to measure the size of machine learning models, but one of the most common is by looking at their parameters.

In the linear regression model just discussed, there would be only one parameter, for square footage. We could make our model better by also showing it the home’s zip code and the number of bathrooms it has, and then it would have three parameters.

It’s hard to say how big most real systems are because that information isn’t usually made public, but a linear regression model might have dozens of parameters, and a basic neural network could range from a few hundred thousand to a few tens of millions of parameters.

GPT-3 has 175 billion parameters, and Google’s Minerva model has 540 billion parameters. It isn’t known how many parameters GPT-4 has, but it’s almost certainly more.

(Note: I say “almost” certainly because better models don’t always have more parameters. They usually do, but it’s not an ironclad rule.)

LLMs Focus On Language.

ChatGPT and its cousins take text as input and produce text as output. This makes them distinct from some of the image-generation tools that are on the market today, such as DALL-E and Midjourney.

It’s worth noting, however, that this might be changing in the future. Though most of what people are using GPT-4 to do revolves around text, technically, the underlying model is multimodal. This means it can theoretically interact with image inputs as well. According to OpenAI’s documentation, support for this feature should arrive in the coming months.

How Are Large Language Models Trained?

Like all machine learning models, LLMs must be trained. We don’t actually know exactly how OpenAI trained the latest GPT models, as they’ve kept those details secret, but we can make some broad comments about how systems like these are generally trained.

Before we get into technical details, let’s frame the overall task that LLMs are trying to perform as a guessing game. Imagine that I start a sentence and leave out the last word, asking you to provide a guess as to how it ends.

Some of these would be fairly trivial; everyone knows that “[i]t was the best of times, it was the worst of _____,” ends with the word “times.” Others would be more ambiguous; “I stopped to pick a flower, and then continued walking down the ____,” could plausibly end with words like “road”, “street”, or “trail.”

For still others, there’d be an almost infinite number of possibilities; “He turned to face the ___,” could end with anything from “firehose” to “firing squad.”

But how is it that you’re able to generate these guesses? How do you know what a good ending to a natural-language sentence sounds like?

The answer is that you’ve been “training” for this task your entire life. You’ve been listening to sentences, reading and writing sentences, or thinking in sentences for most of your waking hours, and have therefore developed a sense of how they work.

The process of training an LLM differs in many specifics, but at a high level, it’s learning to do the same thing. A model like GPT-4 is fed gargantuan amounts of textual data from the internet or other sources, and it learns a statistical distribution that allows it to predict which words come next.

At first, it’ll have no idea how to end the sentence “[i]t was the best of times, it was the worst of ____.” But as it sees more and more examples of human-generated textual content, it improves. It discovers that when someone writes “red, orange, yellow, green, blue, indigo, ______”, the next sequence of letters is probably “violet”. It begins to be more sensitive to context, discovering that the words “bat”, “diamond”, and “plate” are probably occurring in a discussion about baseball and not the weirdest Costco you’ve ever been to.

It’s precisely this nuance that makes advanced LLMs suitable for applications such as customer service.

They’re not simply looking up pre-digested answers to questions, they’re learning a function big enough to account for the subtleties of a specific customer’s specific problem. They still don’t do this job perfectly, but they’ve made remarkable progress, which is why so many companies are looking at integrating them.

Getting into the GPT-weeds

The discussion so far is great for building a basic intuition for how LLMs are trained, but this is a deep dive, so let’s talk technical specifics.

Though we don’t know much about GPT-4, earlier models like GPT and GPT-2 have been studied in great detail. By understanding how they work, we can cultivate a better grasp of cutting-edge models.

When an LLM is trained, it’s fed a great deal of text data. It will grab samples from this data, and try to predict the next token in its sample. To make our earlier explanation easier to understand we implied that a token is a word, but that’s not quite right. A token can be a word, an individual letter, or “sub words”, i.e. small chunks of letters and spaces.

This process is known as “self-supervised learning” because the model can assess its own accuracy by checking its predicted next token against the actual next token in the dataset it’s training on.

At first, its accuracy is likely to be very bad. But as it trains its internal parameters (remember those?) are tuned with an optimizer such as stochastic gradient descent, and it gets better.

One of the crucial architectural building blocks of LLMs is the transformer.

A full discussion of transformers is well beyond the scope of this piece, but the most important thing to know is that transformers can use “attention” to model more complex relationships in language data.

For example: in a sentence like “the dog didn’t chase the cat because it was too tired”, every human knows that “it” refers to the dog and not the cat. Earlier approaches to building language models struggled with such connections in sentences that were longer than a few words, but using attention, transformers can handle them with ease.

In addition to this obvious advantage, transformers have found widespread use in deep learning applications such as language models because they’re easy to parallelize, meaning that training times can be reduced.

Building On Top Of Large Language Models

Out-of-the-box LLMs are pretty powerful, but it’s often necessary to tweak them for specific applications such as enterprise bots. There are a few ways of doing this, and we’re going to confine ourselves to two major approaches: fine-tuning and prompt engineering.

First up, it’s possible to fine-tune some of these models. Fine-tuning an LLM involves providing a training set and letting the model update its internal weights to perform better on a specific task. 

Next, the emerging discipline of prompt engineering refers to the practice of systematically crafting the text fed to the model to get it to better approximate the behavior you want.

LLMs can be surprisingly sensitive to small changes in words, phrases, and context; the job of a prompt engineer, therefore, is to develop a feel for these sensitivities and construct prompts in a way that maximizes the performance of the LLM.

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How Can Large Language Models Be Used In Business?

There is a new gold rush in applying AI to business use cases.

For starters, given how good they are at generating text, they’re being deployed to write email copy, blog posts, and social media content, to text or survey customers, and to summarize text.

LLMs are also being used in software development. Tools like Replit’s Ghostwriter are already dramatically improving developer productivity in a variety of domains, from web development to machine learning.

What Are The “LLiMitations” Of LLMs?

For all their power, LLMs have turned out to have certain well-known limitations. To begin with, LLMs are capable of being toxic, harmful, aggressive, and biased.

Though heroic efforts have been made to train this behavior out with techniques such as reinforcement learning from human feedback, it’s possible that it can reemerge under the right conditions.

This is something you should take into account before giving customers access to generative AI offerings.

Another oft-discussed limitation is the tendency of LLMs to “invent” facts. Remember, an LLM is just trying to predict sequences of tokens, and there’s no reason it couldn’t output a sequence of text like “Dr. Micha Sartorius, professor of applied computronics at Santa Grega University”, even though this person, field, and university are fictitious.

This, too, is something you should be cognizant of before letting customers interact with generative AI.

At Quiq, we harness the power of LLMs’ language-generating capabilities, while putting strict guardrails in place to prevent these risks that are inherent to public-facing generative AI.

Should You Be Using Large Language Models?

LLMs are a remarkable engineering achievement, having been trained on vast amounts of human text and able to generate whole conversations, working code, and more.

No doubt, some of the fervor around LLMs will end up being hype. Nevertheless, the technology has been shown to be incredibly powerful, and it is unlikely to go anywhere. If you’re interested in learning about how to integrate generative AI applications like Quiq’s into your business, schedule a demo with us today!

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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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Agent Efficiency: How to Collect Better Metrics

Posted on Michael Hartsog

Your contact center experience has a direct impact on your bottom line. A positive customer experience can nudge them toward a purchase, encourage repeat business, or turn them into loyal brand advocates.

But a bad run-in with your contact center? That can turn them off of your business for life.

No matter your industry, customer service plays a vital role in financial success. While it’s easy to look at your contact center as an operational cost, it’s truly an investment in the future of your business.

To maximize your return on investment, your contact center must continually improve. That means tracking contact center effectiveness and agent efficiency is critical.

But before you make any changes, you need to understand how your customer service center currently operates. What’s working? What needs improvement? And what needs to be cut?

Let’s examine how contact centers can measure customer service performance and boost efficiency.

What metrics should you monitor?

The world of contact center metrics is overwhelming—to say the least. There are hundreds of data points to track to assess customer satisfaction, agent effectiveness, and call center success.

But to make meaningful improvements, you need to begin with a few basic metrics. Here are three to start with.

1. Response time.

Response time refers to how long, on average, it takes for a customer to reach an agent. Reducing the amount of time it takes to respond to customers can increase customer satisfaction and prevent customer abandonment.

Response time is a top factor for customer satisfaction, with 83% expecting to interact with someone immediately when they contact a company, according to Salesforce’s State of the Connected Customer report.

When using response time to measure agent efficiency, have different target goals set for different channels. For example, a customer calling in or using web chat will expect an immediate response, while an email may have a slightly longer turnaround. Typically, messaging channels like SMS text fall somewhere in between.

If you want to measure how often your customer service team meets your target response times, you can also track your service level. This metric is the percentage of messages and calls answered by customer service agents within your target time frame.

2. Agent occupancy.

Agent occupancy is the amount of time an agent spends actively occupied on a customer interaction. It’s a great way to quickly measure how busy your customer service team is.

An excessively low occupancy suggests you’ve hired more agents than contact volume demands. At the same time, an excessively high occupancy may lead to agent burnout and turnover, which have their own negative effects on efficiency.

3. Customer satisfaction.

The most important contact center performance metric, customer satisfaction, should be your team’s main focus. Customer satisfaction, or CSAT, typically asks customers one question: How satisfied are you with your experience?

Customers respond using a numerical scale to rate their experience from very dissatisfied (0 or 1) to very satisfied (5). However, the range can vary based on your business’s preferences.

You can calculate CSAT scores using this formula:

Number of satisfied customers ÷ total number of respondents x 100 = CSAT

CSAT’s a great first metric to measure since it’s extremely important in measuring your agents’ effectiveness, and it’s easy for customers to complete.

There are lots of options for measuring different aspects of customer satisfaction, like customer effort score and Net Promoter Score®. Whichever you choose, ensure you use it consistently for continuous customer input.

Bonus tip: Capturing customer feedback and agent performance data is easier with contact center software. Not only can the software help with customer relationship management, but it can facilitate customer surveys, track agent data, and more.

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How to assess contact center metrics.

Once you’ve measured your current customer center operations, you can start assessing and taking action to improve performance and boost customer satisfaction. But looking at the data isn’t as easy as it seems. Here are some things to keep in mind as you start to base decisions on your numbers.

Figure out your reporting methods.

How will you gather this information? What timeframes will you measure? Who’s included in your measurements? These are just a few questions you need to answer before you can start analyzing your data.

Contact center software, or even more advanced conversational AI platforms like Quiq, can help you track metrics and even put together reports that are ready for your management team to analyze and take action on.

Analyze data over time.

When you’re just starting out, it can be hard to contextualize your data. You need benchmarks to know whether your CSAT rating or occupancy rates are good or bad. While you can start with industry benchmarks, the most effective way to analyze data is to measure it against yourself over periods of time.

It takes months or even years for trends to reveal themselves. Start with comparative measurements and then work your way up. Month-over-month data or even quarter-over-quarter can give you small windows into what’s working and what’s not working. Just leave the big department-wide changes until you’ve collected enough data for it to be meaningful.

Don’t forget about context.

You can’t measure contact center metrics in a silo. Make sure you look at what’s going on throughout your organization and in the industry as a whole before making any changes. For example, a drop in customer response time might have to do with the influx of messages caused by a faulty product.

While collecting the data is easy, analyzing it and drawing conclusions is much more difficult. Keep the whole picture in mind when making any important decisions.
How to improve call center agent efficiency.
Now that you have the numbers, you can start making changes to improve your agent efficiency. Start with these tips.

Make incremental changes.

Don’t be tempted to make wide-reaching changes across your entire contact center team when you’re not happy with the data. Select specific metrics to target and make incremental changes that move the needle in the right direction.

For example, if your agent occupancy rates are high, don’t rush to add new members to your team. Instead, see what improvements you can make to agent efficiency. Maybe there’s some call center software you can invest in that’ll improve call turnover. Or perhaps all your team needs is some additional training on how to speed up their customer interactions. No matter what you do, track your changes.

Streamline backend processes.

Agents can’t perform if they’re constantly searching for answers on slow intranets or working with outdated information. Time spent fighting with old technology is time not spent serving your contact center customers.

Now’s the perfect time to consider a conversational platform that allows your customers to reach out using the preferred channel but still keeps the backend organized and efficient for your team.

Agents can bounce back and forth between messaging channels without losing track of conversations. Customers get to chat with your brand how they want, where they want, and your team gets to preserve the experience and deliver snag-free customer service.

Improve agent efficiency with Quiq’s Conversational AI Platform

If you want to improve your contact center’s efficiency and customer satisfaction ratings, Quiq’s conversational customer engagement software is your new best friend.

Quiq’s software enables agents to manage multiple conversations simultaneously and message customers across channels, including text and web chat. By giving customers more options for engaging with customer service, Quiq reduces call volume and allows contact center agents to focus on the conversations with the highest priority.

How To Be The Leader Of Personalized CX In Your Industry

Posted on Cristina Bravo-Olmo

Customer expectations are evolving alongside AI technology, at an unprecedented pace. People are more informed, connected, and demanding than ever before, and they expect nothing less than exceptional customer experiences (CX) from the brands they interact with.

This is where personalized customer experience comes in.

By tailoring CX to individual customers’ needs, preferences, and behaviors, businesses can create more meaningful connections, build loyalty, and drive revenue growth.
In this article, we will explore the power of personalized CX in industries and how it can help businesses stay ahead of the curve.

What is Personalized CX?

Personalized CX refers to the process of tailoring customer experiences to individual customers based on their unique needs, preferences, and behaviors. This involves using customer data and insights to create targeted and relevant interactions across multiple touchpoints, such as websites, mobile apps, social media, and customer service channels.

Personalization can take many forms, from simple tactics like using a customer’s name in a greeting to more complex strategies like recognizing that they are likely to be asking a question about the order that was delivered today. The goal is to create a seamless and consistent experience that makes customers feel valued and understood.

Why is Personalized CX Important?

Personalized CX has become increasingly important in industries for several reasons:

1. Rising Customer Expectations

Today’s customers expect personalized experiences across all industries, from retail and hospitality to finance and healthcare. In fact, according to a survey by Epsilon, 80% of consumers are more likely to do business with a company if it offers personalized experiences.

2. Increased Competition

As industries become more crowded and competitive, businesses need to find new ways to differentiate themselves. Personalized CX can help brands stand out by creating a unique and memorable experience that sets them apart from their competitors.

3. Improved Customer Loyalty and Retention

Personalized CX can help businesses build stronger relationships with their customers by creating a sense of loyalty and emotional connection. According to a survey by Accenture, 75% of consumers are more likely to buy from a company that recognizes them by name, recommends products based on past purchases, or knows their purchase history.

4. Increased Revenue

By providing personalized CX, businesses can also increase revenue by creating more opportunities for cross-selling and upselling. According to a study by McKinsey, personalized recommendations can drive 10-30% of revenue for e-commerce businesses.

Industries That Can Benefit From Personalized CX

Personalized CX can benefit almost any industry, but some industries are riper for personalization than others.

Here are some industries that can benefit the most from personalized CX:

1. Retail

Retail is one of the most obvious industries that can benefit from personalized CX. By using customer data and insights, retailers can create tailored product recommendations and personalized support based on products purchased and current order status.

2. Hospitality

In the hospitality industry, personalized CX can create a more memorable and enjoyable experience for guests. From personalized greetings to customized room amenities, hospitality businesses can use personalization to create a sense of luxury and exclusivity.

3. Healthcare

Personalized CX is also becoming increasingly important in healthcare. By tailoring healthcare experiences to individual patients’ needs and preferences, healthcare providers can create a more patient-centered approach that improves outcomes and satisfaction.

4. Finance

In the finance industry, personalized CX can help businesses create more targeted and relevant offers and services. By using customer data and insights, financial institutions can offer personalized recommendations for investments, loans, and insurance products.

Best Practices for Implementing Personalized CX in Industries

Implementing personalized CX requires a strategic approach and a deep understanding of customers’ preferences and behaviors.

Here are some best practices for implementing personalized CX in industries:

1. Collect and Use Customer Data Wisely

Collecting customer data is essential for personalized CX, but it’s important to do so in a way that respects customers’ privacy and preferences. Businesses should be transparent about the data they collect and how they use it, and give customers the ability to opt out of data collection.

2. Use Technology to Scale Personalization

Personalizing CX for every individual customer can be a daunting task, especially for large businesses. Using technology, such as machine learning algorithms and artificial intelligence (AI), can help businesses scale personalization efforts and make them more efficient.

3. Be Relevant and Timely

Personalized CX is only effective if it’s relevant and timely. Businesses should use customer data to create targeted and relevant offers, messages, and interactions that resonate with customers at the right time.

4. Focus on the Entire Customer Journey

Personalization shouldn’t be limited to a single touchpoint or interaction. To create a truly personalized CX, businesses should focus on the entire customer journey, from awareness to purchase and beyond.

5. Continuously Test and Optimize

Personalized CX is a continuous process that requires constant testing and optimization. Businesses should use data and analytics to track the effectiveness of their personalization efforts and make adjustments as needed.

Challenges of Implementing Personalized CX in Industries

While the benefits of personalized CX are clear, implementing it in industries can be challenging. Here are some of the challenges businesses may face:

1. Data Privacy and Security Concerns

Collecting and using customer data for personalization raises concerns about data privacy and security. Businesses must ensure they are following best practices for data collection, storage, and usage to protect their customers’ information.

2. Integration with Legacy Systems

Personalization requires a lot of data and advanced technology, which may not be compatible with legacy systems. Businesses may need to invest in new infrastructure and systems to support personalized CX.

3. Lack of Skilled Talent

Personalized CX requires a skilled team with expertise in data analytics, machine learning, and AI. Finding and retaining this talent can be a challenge for businesses, especially smaller ones.

4. Resistance to Change

Implementing personalized CX requires significant organizational change, which can be met with resistance from employees and stakeholders. Businesses must communicate the benefits of personalization and provide training and support to help employees adapt.

Personalized CX is no longer a nice-to-have; it’s a must-have for businesses that want to stay competitive in today’s digital age. By tailoring CX to individual customers’ needs, preferences, and behaviors, businesses can create more meaningful connections, build loyalty, and drive revenue growth. While implementing personalized CX in industries can be challenging, the benefits far outweigh the costs.

Live Chat: Is it Effective for Online Customer Service?

Posted on The Quiq Team

Implementing new technology in any department can be scary—but it’s even scarier when it’s customer-facing. We’re sure you’ve heard of live web chat, and even used it in your personal life, but you’re hesitant to launch it in your customer service team.

Will your customers want to use it? What will your customer service agents think? Is web chat (also known as live chat) even effective?

With a sluggish economy on the horizon for the foreseeable future, you can’t afford to implement new technology without doing your due diligence and putting in the research. Should you implement web chat?

Let’s dive in.

What is live web chat?

Web chat, aka live chat, is a two-way, text-based conversation that happens on your website. A chat box typically lives on the bottom right corner of your site. It pops up when the chat is active but can be minimized when not in use.

The best part? The conversation follows your customer as they navigate across different pages of your website.

While it’s often called live chat, the name is a big misnomer. A major benefit of live chat is its immediacy, but it doesn’t always have to happen in real-time. With the right conversational platform, you can have asynchronous conversations with your customers as they come and go from your site.

What’s an asynchronous conversation? It’s a conversation where both parties don’t have to be present at the same time. It’s also typically characterized by not having a specific beginning or end. So instead of a customer coming to you with a specific problem, getting their answer, and leaving the conversation, asynchronous conversations are much more fluid. Customers can ask a question as they’re perusing your site, get distracted by daily life, and come back with their questions answered.

What the statistics say.

The numbers speak for themselves when it comes to the effectiveness of web chat. According to Shopify, 41% of consumers want live chat while shopping online, and Salesforce reports 42% prefer it. While those numbers are impressive by themselves, they don’t tell the full story.

When it comes to customer satisfaction, live chat beats out almost every other form of customer service communication. Phone calls (talk) are the only medium that customers report having higher satisfaction ratings.

While it seems unusual at first blush, it makes sense when you consider response time and first-resolution time—two things that customers often rank as high on their list of important customer service factors.

satisfaction by channel

Source: Zendesk

Better customer satisfaction ultimately turns into higher customer conversion rates and more positive reviews, which is often a large contributing factor to business success over time.

It is easy to understand why live chat increases customer satisfaction rates when your customers know they can get the convenience of service at their fingertips rather than waiting on hold or sending an email and waiting for a response. The immediacy of live chat is the more modern way customers get answers and complete transactions.

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What are the benefits of using live chat?

Customer service effectiveness can be measured by changes in consumer satisfaction and conversion rates. Live chat is a boon to both customers and agents because it offers both sides a more frictionless way to engage. Take a look at some of the benefits.

Provide immediate, intuitive service.

Just because a conversation has the opportunity to be asynchronous doesn’t mean you should make your customers wait. According to Salesforce’s State of the Connected Customer report, 83% of customers expect to interact with someone immediately when contacting a business. Design your asynchronous live chat strategy around flexibility for your customers, not more time for your agents to respond.

Create customer-centric experiences.

As e-commerce replaces more traditional in-person shopping experiences, customers want more convenient and personalized service. According to Zendesk, 71% of customers demand natural, conversational experiences. Live chat is the perfect medium to deliver that.

The chat feature is naturally more conversational than email and other cumbersome communication methods. Live chat also won’t interrupt their shopping experience—something 66% of customers prefer.

Boost your bottom line.

Did you know that 78% of surveyed shoppers have abandoned their carts at least once? Web chat can help prevent cart abandonment by making it easy to ask quick questions and enabling sales and service agents to interact proactively with customers. Helping customers when they’re actively engaged on your site helps reduce the likelihood of them bouncing to another page.

Increase brand loyalty.

Businesses ready to answer their customer’s questions quickly and in a friendly way begin to create a lasting bond built on trust and respect. Live chat is an open door to consumers who want to engage with your brand. Brands that make themselves available more often to their customers will reap the rewards of customers who feel that their time and attention are valued by your brand, earning their repeat business and referrals.

How do you make live chat more effective?

Live chat is effective—and even more so when paired with Quiq’s AI conversational platform. But like any tool, your success depends on how well you use it. Here are a few strategies to improve your live chat experience.

Have conversations with context.

Context is a vital piece of the puzzle when interacting with customers online. In fact, 70% of customers expect anyone they interact with to know their shopping history.

Quiq’s conversational AI platform helps conversations follow the user no matter which channels they use. It also ensures that every customer service agent has the customer’s history through integrations with ERPs and CRMs.

Pair chatbots with live chat.

Companies that enable chatbots, as well as human agents, as part of their live web chat strategy are able to manage an even higher number of customer inquiries. Companies like Brinks Home Security have enabled multiple purpose-built chatbots that route customers to the most appropriate queue or agent, automate the referral process, and even boost conversions of promotional offers.

A dazzling example of live chat in action.

Quiq customer and diamond dealer Blue Nile knew their online shopping experience needed to be top-tier for such a luxury product. When customers interacted with their diamond experts, they converted at a rate 15x higher than when they visited the website alone. So Blue Nile knew they needed to create the best live chat experience possible.

With help from Quiq, they designed a chatbot to send customers to the appropriate person for their needs. If they had a post-purchase question, they went to customer service. But if they had a product question, they went directly to a Blue Nile Diamond Expert. This increased their sales interactions by 70%.

70% growth in sales interactions and 35% increase in successful sales transactions using Quiq web chat and chatbot.

Launch your live chat strategy with Quiq

No matter what industry you work in, live chat can have a major influence on your business’s success. Get in touch with us today to learn more about how Quiq can help enhance your customer service experience.