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Month: February 2024

Retrieval Augmented Generation – Ultimate Guide

Posted on J.R. Rettenmeyer

A lot has changed since the advent of large language models a little over a year ago. But, incredibly, there are already many attempts at extending the functionality of the underlying technology.

One broad category of these attempts is known as “tool use”, and consists of augmenting language models by giving them access to things like calculators. Stories of these models failing at simple arithmetic abound, and the basic idea is that we can begin to shore up their weaknesses by connecting them to specific external resources.

Because these models are famously prone to “hallucinating” incorrect information, the technique of retrieval augmented generation (RAG) has been developed to ground model output more effectively. So far, this has shown promise as a way of reducing hallucinations and creating much more trustworthy replies to queries.

In this piece, we’re going to discuss what retrieval augmented generation is, how it works, and how it can make your models even more robust.

Understanding Retrieval Augmented Generation

To begin, let’s get clear on exactly what we’re talking about. The next few sections will overview retrieval augmented generation, break down how it works, and briefly cover its myriad benefits.

What is Retrieval Augmented Generation?

Retrieval augmented generation refers to a large and growing cluster of techniques meant to help large language models ground their output in facts obtained from an external source.

By now, you’re probably aware that language models can do a remarkably good job of generating everything from code to poetry. But, owing to the way they’re trained and the way they operate, they’re also prone to simply fabricating confident-sounding nonsense. If you ask for a bunch of papers about the connection between a supplement and mental performance, for example, you might get a mix of real papers and ones that are completely fictitious.

If you could somehow hook the model up to a database of papers, however, then perhaps that would ameliorate this tendency. That’s where RAG comes in.

We will discuss some specifics in the next section, but in the broadest possible terms, you can think of RAG as having two components: the generative model, and a retrieval system that allows it to augment its outputs with data obtained from an authoritative external source.

The difference between using a foundation model and using a foundation model with RAG has been likened to the difference between taking a closed-book and an open-booked test – the metaphor is an apt one. If you were to poll all your friends about their knowledge of photosynthesis, you’d probably get a pretty big range of replies. Some friends would remember a lot about the process from high school biology, while others would barely even know that it’s related to plants.

Now, imagine what would happen if you gave these same friends a botany textbook and asked them to cite their sources. You’d still get a range of replies, of course, but they’d be far more comprehensive, grounded, and replete with up-to-date details. [1]

How RAG Works

Now that we’ve discussed what RAG is, let’s talk about how it functions. Though there are many subtleties involved, there are only a handful of overall steps.

First, you have to create a source of external data or utilize an existing one. There are already many such external resources, including databases filled with scientific papers, genomics data, time series data on the movements of stock prices, etc., which are often accessible via an API. If there isn’t already a repository containing the information you’ll need, you’ll have to make one. It’s also common to hook generative models up to internal technical documentation, of the kind utilized by e.g. contact center agents.

Then, you’ll have to do a search for relevancy. This involves converting queries into vectors, or numerical representations that capture important semantic information, then matching that representation against the vectorized contents of the external data source. Don’t worry too much if this doesn’t make a lot of sense, the important thing to remember is that this technique is far better than basic keyword matching at turning up documents related to a query.

With that done, you’ll have to augment the original user query with whatever data came up during the relevancy search. In the systems we’ve seen this all occurs silently, behind the scenes, with the user being unaware that any such changes have been made. But, with the additional context, the output generated by the model will likely be much more grounded and sensible. Modern RAG systems are also sometimes built to include citations to the specific documents they drew from, allowing a user to fact-check the output for accuracy.

And finally, you’ll need to think continuously about whether the external data source you’ve tied your model to needs to be updated. It doesn’t do much good to ground a model’s reply if the information it’s using is stale and inaccurate, so this step is important.

The Benefits of RAG

Language models equipped with retrieval augmented generation have many advantages over their more fanciful, non-RAG counterparts. As we’ve alluded to throughout, such RAG models tend to be vastly more accurate. RAG, of course, doesn’t guarantee that a model’s output will be correct. They can still hallucinate, just as one of your friends reading a botany book might misunderstand or misquote a passage. Still, it makes hallucinations far less prevalent and, if the model adds citations, gives you what you need to rectify any errors.

For this same reason, it’s easier to trust a RAG-powered language model, and they’re (usually) easier to use. As we said above a lot of the tricky technical detail is hidden from the end user, so all they see is a better-grounded output complete with a list of documents they can use to check that the output they’ve gotten is right.

Applications of Retrieval Augmented Generation

We’ve said a lot about how awesome RAG is, but what are some of its primary use cases? That will be our focus here, over the next few sections.

Enhancing Question Answering Systems

Perhaps the most obvious way RAG could be used is to supercharge the function of question-answering systems. This is already a very strong use case of generative AI, as attested to by the fact that many people are turning to tools like ChatGPT instead of Google when they want to take a first stab at understanding a new subject.

With RAG, they can get more precise and contextually relevant answers, enabling them to overcome hurdles and progress more quickly.

Of course, this dynamic will also play out in contact centers, which are more often leaning on question-answering systems to either make their agents more effective, or to give customers the resources they need to solve their own problems.

Chatbots and Conversational Agents

Chatbots are another technology that could be substantially upgraded through RAG. Because this is so closely related to the previous section we’ll keep our comments brief; suffice it to say, a chatbot able to ground its replies in internal documentation or a good external database will be much better than one that can’t.

Revolutionizing Content Creation

Because generative models are so, well, generative, they’ve already become staples in the workflows of many creative sorts, such as writers, marketers, etc. A writer might use a generative model to outline a piece, paraphrase their own earlier work, or take the other side of a contentious issue.

This, too, is a place where RAG shines. Whether you’re tinkering with the structure of a new article or trying to build a full-fledged research assistant to master an arcane part of computer science, it can only help to have more factual, grounded output.

Recommendation Systems

Finally, recommendation systems could see a boost from RAG. As you can probably tell from their name, recommendation systems are machine-learning tools that find patterns in a set of preferences and use them to make new recommendations that fit that pattern.

With grounding through RAG, this could become even better. Imagine not only having recommendations, but also specific details about why a particular recommendation was made, to say nothing of recommendations that are tied to a vast set of external resources.

Conclusion

For all the change we’ve already seen from generative AI, RAG has yet more more potential to transform our interaction with AI. With retrieval augmented generation, we could see substantial upgrades in the way we access information and use it to create new things.

If you’re intrigued by the promise of generative AI and the ways in which it could supercharge your contact center, set up a demo of the Quiq platform today!

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Footnotes

[1] This assumes that the book you’re giving them is itself up-to-date, and the same is true with RAG. A generative model is only as good as its data.

Leveraging Agent Insights to Boost Efficiency and Performance

Posted on The Quiq Team

In the ever-evolving customer service landscape, the role of contact center agents cannot be overstated. As the frontline representatives of a company, their performance directly impacts the quality of customer experience, influencing customer loyalty and brand reputation.

However, the traditional approach to managing agent performance – relying on periodic reviews and supervisor observations – has given way to a more sophisticated, data-driven strategy. For this reason, managing agent performance with a method that leverages the rich data generated by agent interactions to enhance service delivery, agent satisfaction, and operational efficiency is becoming more important all the time.

This article delves into this approach. We’ll begin by examining its benefits from three critical perspectives – the customer, the agent, and the contact center manager – before turning to a more granular breakdown of how you can leverage it in your contact center.

Why is it Important to Manage Agent Performance with Insights?

First, let’s start by justifying this project. While it’s true that very few people today would doubt the need to track some data related to what agents are doing all day, it’s still worth saying a few words about why it really is a crucial part of running a contact center.

To do this, we’ll focus on how three groups are impacted when agent performance is managed through insights: customers, the agents themselves, and contact center managers.

It’s Good for the Customers

The primary beneficiary of improved agent performance is the customer. Contact centers can tailor their service strategies by analyzing agent metrics to better meet customer needs and preferences. This data-driven approach allows for identifying common issues, customer pain points, and trends in customer behavior, enabling more personalized and effective interactions.

As agents become more adept at addressing customer needs swiftly and accurately, customer satisfaction levels rise. This enhances the individual customer’s experience and boosts the overall perception of the brand, fostering loyalty and encouraging positive word-of-mouth.

It’s Good for the Agents

Agents stand to gain immensely from a management strategy focused on data-driven insights. Firstly, performance feedback based on concrete metrics rather than subjective assessments leads to a fairer, more transparent work environment.

Agents receive specific, actionable feedback that helps them understand their strengths and which areas need improvement. This can be incredibly motivating and can drive them to begin proactively bolstering their skills.

Furthermore, insights from performance data can inform targeted training and development programs. For instance, if data indicates that an agent excels in handling certain inquiries but struggles with others, their manager can provide personalized training to bridge this gap. This helps agents grow professionally and increases their job satisfaction as they become more competent and confident in their roles.

It’s Good for Contact Center Managers

For those in charge of overseeing contact centers, managing agents through insights into their performance offers a powerful tool for cultivating operational excellence. It enables a more strategic approach to workforce management, where decisions are informed by data rather than gut feeling.

Managers can identify high performers and understand the behaviors that lead to success, allowing them to replicate these practices across the team. Intriguingly, this same mechanism is also at play in the efficiency boost seen by contact centers that adopt generative AI. When such centers train a model on the interactions of their best agents, the knowledge in those agents’ heads can be incorporated into the algorithm and utilized by much more junior agents.

The insights-driven approach also aids in resource allocation. By understanding the strengths and weaknesses of their team, managers can assign agents to the tasks they are most suited for, optimizing the center’s overall performance.

Additionally, insights into agent performance can highlight systemic issues or training gaps, providing managers with the opportunity to make structural changes that enhance efficiency and effectiveness.

Moreover, using agent insights for performance management supports a culture of continuous improvement. It encourages a feedback loop where agents are continually assessed, supported, and developed, driving the entire team towards higher performance standards. This improves the customer experience and contributes to a positive working environment where agents feel valued and empowered.

In summary, managing performance by tracking agent metrics is a holistic strategy that enhances the customer experience, supports agent development, and empowers managers to make informed decisions.

It fosters a culture of transparency, accountability, and continuous improvement, leading to operational excellence and elevated service standards in the contact center.

How to Use Agent Insights to Manage Performance

Now that we know what all the fuss is about, let’s turn to addressing our main question: how to use agent insights to correct, fine-tune, and optimize agent performance. This discussion will center specifically around Quiq’s Agent Insights tool, which is a best-in-class analytics offering that makes it easy to figure out what your agents are doing, where they could improve, and how that ultimately impacts the customers you serve.

Managing Agent Availability

To begin with, you need a way of understanding when your agents are free to handle an issue and when they’re preoccupied with other work. The three basic statuses an agent can have are “available,” “current conversations” (i.e. only working on the current batch of conversations), and “unavailable.” All three of these can be seen through Agent Insights, which allows you to select from over 50 different metrics, customizing and saving different views as you see fit.

The underlying metrics that go into understanding this dimension of agent performance are, of course, time-based. In essence, you want to evaluate the ratios between four quantities: the time the agent is available, the time the agent is online, the time the agent spends in a conversation, and the time an agent is unavailable.

As you’re no doubt aware, you don’t necessarily want to maximize the amount of time an agent spends in conversations, as this can quickly lead to burnout. Rather, you want to use these insights into agent performance to strike the best, most productive balance possible.

Managing Agent Workload

A related phenomenon you want to understand is the kind of workload your agents are operating under. The five metrics that underpin this are:

  1. Availability
  2. Number of completions per hour your agents are managing
  3. Overall utilization (i.e. the percentage of an agent’s available conversation limit they have filled in a given period).
  4. Average workload
  5. The amount of time agents spend waiting for a customer response.

All of this can be seen in Agent Insights. This view allows you to do many things to hone in on specific parts of your operation. You can sort by the amount of time your agents spend waiting for a reply from a customer, for example, or segment agents by e.g. role. If you’re seeing high waiting and low utilization, that means you are overstaffed and should probably have fewer agents.

If you’re seeing high waiting and high utilization, by contrast, you should make sure your agents know inactive conversations should be marked appropriately.

As with the previous section, you’re not necessarily looking to minimize availability or maximize completions per hour. You want to make sure that agents are working at a comfortable pace, and that they have time between issues to reflect on how they’re doing and think about whether they want to change anything in their approach.

But with proper data-driven insights, you can do much more to ensure your agents have the space they need to function optimally.

Managing Agent Efficiency

Speaking of functioning optimally, the last thing we want to examine is agent efficiency. By using Agent Insights, we can answer questions such as how well new agents are adjusting to their roles, how well your teams are working together, and how you can boost each agent’s output (without working them too hard).

The field of contact center analytics is large, but in the context of agent efficiency, you’ll want to examine metrics like completion rate, completions per hour, reopen rate, missed response rate, missed invitation rate, and any feedback customers have left after interacting with your agents.

This will give you an unprecedented peek into the moment-by-moment actions agents are taking, and furnish you with the hard facts you need to help them streamline their procedures. Imagine, for example, you’re seeing a lot of keyboard usage. This means the agent is probably not operating as efficiently as they could be, and you might be able to boost their numbers by training them to utilize Quiq’s Snippets tool.

Or, perhaps you’re seeing a remarkably high rate of clipboard usage. In that case, you’d want to look over the clipboard messages your agents are using and consider turning them into snippets, where they’d be available to everyone.

The Modern Approach to Managing Agents

Embracing agent insights for performance management marks a transformative step towards achieving operational excellence in contact centers. This data-driven approach not only elevates the customer service experience but also fosters a culture of continuous improvement and empowerment among agents.

By leveraging tools like Quiq’s Agent Insights, managers can unlock a comprehensive understanding of agent availability, workload, and efficiency, enabling informed decisions that benefit both the customer and the service team.

If you’re intrigued by the possibilities, contact us to schedule a demo today!

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6 Questions to Ask Generative AI Vendors You’re Evaluating

Posted on The Quiq Team

With all the power exhibited by today’s large language models, many businesses are scrambling to leverage them in their offerings. Enterprises in a wide variety of domains – from contact centers to teams focused on writing custom software – are adding AI-backed functionality to make their users more productive and the customer experience better.

But, in the rush to avoid being the only organization not using the hot new technology, it’s easy to overlook certain basic sanity checks you must perform when choosing a vendor. Today, we’re going to fix that. This piece will focus on several of the broad categories of questions you should be asking potential generative AI providers as you evaluate all your options.

This knowledge will give you the best chance of finding a vendor that meets your requirements, will help you with integration, and will ultimately allow you to better serve your customers.

These are the Questions you Should ask Your Generative AI Vendor

Training large language models is difficult. Besides the fact that it requires an incredible amount of computing power, there are also hundreds of tiny little engineering optimizations that need to be made along the way. This is part of the reason why all the different language model vendors are different from one another.

Some have a longer context window, others write better code but struggle with subtle language-based tasks, etc. All of this needs to be factored into your final decision because it will impact how well your vendor performs for your particular use case.

In the sections that follow, we’ll walk you through some of the questions you should raise with each vendor. Most of these questions are designed to help you get a handle on how easy a given offering will be to use in your situation, and what integrating it will look like.

1. What Sort of Customer Service Do You Offer?

We’re contact center and customer support people, so we understand better than anyone how important it is to make sure users know what our product is, what it can do, and how we can help them if they run into issues.

As you speak with different generative AI vendors, you’ll want to probe them about their own customer support, and what steps they’ll take to help you utilize their platform effectively.

For this, just start with the basics by figuring out the availability of their support teams – what hours they operate in, whether they can accommodate teams working in multiple time zones, and whether there is an option for 24/7 support if a critical problem arises.

Then, you can begin drilling into specifics. One thing you’ll want to know about is the channels their support team operates through. They might set up a private Slack channel with you so you can access their engineers directly, for example, or they might prefer to work through email, a ticketing system, or a chat interface. When you’re discussing this topic, try to find out whether you’ll have a dedicated account manager to work with.

You’ll also want some context on the issue resolution process. If you have a lingering problem that’s not being resolved, how do you go about escalating it, and what’s the team’s response time for issues in general?

Finally, it’s important that the vendors have some kind of feedback mechanism. Just as you no doubt have a way for clients to let you know if they’re dissatisfied with an agent or a process, the vendor you choose should offer a way for you to let them know how they’re doing so they can improve. This not only tells you they care about getting better, it also indicates that they have a way of figuring out how to do so.

2. Does Your Team Offer Help with Setting up the Platform?

A related subject is the extent to which a given generative AI vendor will help you set up their platform in your environment. A good way to begin is by asking what kinds of training materials and resources they offer.

Many vendors are promoting their platforms by putting out a ton of educational content, all of which your internal engineers can use to get up to speed on what those platforms can do and how they function.

This is the kind of thing that is easy to overlook, but you should pay careful attention to it. Choosing a generative AI vendor that has excellent documentation, plenty of worked-out examples, etc. could end up saving you a tremendous amount of time, energy, and money down the line.

Then, you can get clarity on whether the vendor has a dedicated team devoted to helping customers like you get set up. These roles are usually found under titles like “solutions architect”, so be sure to ask whether you’ll be assigned such a person and the extent to which you can expect their help. Some platforms will go to the moon and back to make sure you have everything you need, while others will simply advise you if you get stuck somewhere.

Which path makes the most sense depends on your circumstances. If you have a lot of engineers you may not need more than a little advice here and there, but if you don’t, you’ll likely need more handholding (but will probably also have to pay extra for that). Keep all this in mind as you’re deciding.

3. What Kinds of Integrations Do You Support?

Now, it’s time to get into more technical details about the integrations they support. When you buy a subscription to a generative AI vendor, you are effectively buying a set of capabilities. But those capabilities are much more valuable if you know they’ll plug in seamlessly with your existing software, and they’re even more valuable if you know they’ll plug into software you plan on building later on. You’ve probably been working on a roadmap, and now’s the time to get it out.

It’s worth checking to see whether the vendor can support many different kinds of language models. This involves a nuance in what the word “vendor” means, so let’s unpack it a little bit. Some generative AI vendors are offering you a model, so they’re probably not going to support another company’s model.

OpenAI and Anthropic are examples of model vendors, so if you work with them you’re buying their model and will not be able to easily incorporate someone else’s model.

Other vendors, by contrast, are offering you a service, and in many cases that service could theoretically by powered by many different models.

Quiq’s Conversational CX Platform, for example, supports OpenAI’s GPT models, and we have plans to expand the scope of our integrations to encompass even more models in the future.

Another thing you’re going to want to check on is whether the vendor makes it easy to integrate vector databases into your workflow. Vectors are data structures that are remarkably good at capturing subtle relationships in large datasets; they’re becoming an ever-more-important part of machine learning, as evinced by the fact that there are now a multitude of different vector databases on offer.

The chances are pretty good that you’ll eventually want to leverage a vector database to store or search over customer interactions, and you’ll want a vendor that makes this easy.

Finally, see if the vendor has any case studies you can look at. Quiq has published a case study on how our language services were utilized by LOOP, a car insurance company, to make a far superior customer-service chatbot. The result was that customers were able to get much more personalization in their answers and were able to resolve their problems fully half of the time, without help. This led to a corresponding 55% reduction in tickets, and a customer satisfaction rating of 75% (!) when interacting with the Quiq-powered AI assistant.

See if the vendors you’re looking at have anything similar you can examine. This is especially helpful if the case studies are focused on companies that are similar to yours.

4. How Does Prompt Engineering and Fine-Tuning Work for Your Model?

For many tasks, large language models work perfectly fine on their own, without much special effort. But there are two methods you should know about to really get the most out of them: prompt engineering and fine-tuning.

As you know, prompts are the basic method for interacting with language models. You’ll give a model a prompt like “What is generative AI?”, and it’ll generate a response. Well, it turns out that models are really sensitive to the wording and structure of prompts, and prompt engineers are those who explore the best way to craft prompts to get useful output from a model.

It’s worth asking potential vendors about this because they handle prompts differently. Quiq’s AI Studio encourages atomic prompting, where a single prompt has a clear purpose and intended completion, and we support running prompts in parallel and sequentially. You can’t assume everyone will do this, however, so be sure to check.

Then, there’s fine-tuning, which refers to training a model on a bespoke dataset such that its output is heavily geared towards the patterns found in that dataset. It’s becoming more common to fine-tune a foundational model for specific use cases, especially when those use cases involve a lot of specialized vocabulary such as is found in medicine or law.

Setting up a fine-tuning pipeline can be cumbersome or relatively straightforward depending on the vendor, so see what each vendor offers in this regard. It’s also worth asking whether they offer technical support for this aspect of working with the models.

5. Can Your Models Support Reasoning and Acting?

One of the current frontiers in generative AI is building more robust, “agentic” models that can execute strings of tasks on their way to completing a broader goal. This goes by a few different names, but one that has been popping up in the research literature is “ReAct”, which stands for “reasoning and acting”.

You can get ReAct functionality out of existing language models through chain-of-thought prompting, or by using systems like AutoGPT; to help you concretize this a bit, let’s walk through how ReAct works in Quiq.

With Quiq’s AI Studio, a conversational data model is used to classify and store both custom and standard data elements, and these data elements can be set within and across “user turns”. A single user turn is the time between when a user offers an input to the time at which the AI responds and waits for the next user input.

Our AI can set and reason about the state of the data model, applying rules to take the next best action. Common actions include things like fetching data, running another prompt, delivering a message, or offering to escalate to a human.

Though these efforts are still early, this is absolutely the direction the field is taking. If you want to be prepared for what’s coming without the need to overhaul your generative AI stack later on, ask about how different vendors support ReAct.

6. What’s your Pricing Structure Like?

Finally, you’ll need to talk to vendors about how their prices work, including any available details on licensing types, subscriptions, and costs associated with the integration, use, and maintenance of their solution.

To take one example, Quiq’s licensing is based on usage. We establish a usage pool wherein our customers pre-pay Quiq for a 12-month contract; then, as the customer uses our software money is deducted from that pool. We also have an annual AI Assistant Maintenance fee along with a one-time implementation fee.

Vendors can vary considerably in how their prices work, so if you don’t want to overpay then make sure you have a clear understanding of their approach.

Picking the Right Generative AI Vendor

Language models and related technologies are taking the world by storm, transforming many industries, including customer service and contact center management.

Making use of these systems means choosing a good vendor, and that requires you to understand each vendor’s model, how those models integrate with other tools, and what you’re ultimately going to end up paying.

If you want to see how Quiq stacks up and what we can do for you, schedule a demo with us today!

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Your Guide to Trust and Transparency in the Age of AI

Posted on The Quiq Team

Over the last few years, AI has really come into its own. ChatGPT and similar large language models have made serious inroads in a wide variety of natural language tasks, generative approaches have been tested in domains like music and protein discovery, researchers have leveraged techniques like chain-of-thought prompting to extend the capabilities of the underlying models, and much else besides.

People working in domains like customer service, content marketing, and software engineering are mostly convinced that this technology will significantly impact their day-to-day lives, but many questions remain.

Given the fact that these models are enormous artifacts whose inner workings are poorly understood, one of the main questions centers around trust and transparency. In this article, we’re going to address these questions head-on. We’ll discuss why transparency is important when advanced algorithms are making ever more impactful decisions, and turn our attention to how you can build a more transparent business.

Why is Transparency Important?

First, let’s take a look at why transparency is important in the first place. The next few sections will focus on the trust issues that stem from AI becoming a ubiquitous technology that few understand at a deep level.

AI is Becoming More Integrated

AI has been advancing steadily for decades, and this has led to a concomitant increase in its use. It’s now commonplace for us to pick entertainment based on algorithmic recommendations, for our loan and job applications to pass through AI filters, and for more and more professionals to turn to ChatGPT before Google when trying to answer a question.

We personally know of multiple software engineers who claim to feel as though they’re at a significant disadvantage if their preferred LLM is offline for even a few hours.

Even if you knew nothing about AI except the fact that it seems to be everywhere now, that should be sufficient incentive to want more context on how it makes decisions and how those decisions are impacting the world.

AI is Poorly Understood

But, it turns out there is another compelling reason to care about transparency in AI: almost no one knows how LLMs and neural networks more broadly can do what they do.

To be sure, very simple techniques like decision trees and linear regression models pose little analytical challenge, and we’ve written a great deal over the past year about how language models are trained. But if you were to ask for a detailed account of how ChatGPT manages to create a poem with a consistent rhyme scheme, we couldn’t tell you.

And – as far as we know – neither could anyone else.

This is troubling; as we noted above, AI has become steadily more integrated into our private and public lives, and that trend will surely only accelerate now that we’ve seen what generative AI can do. But if we don’t have a granular understanding of the inner workings of advanced machine-learning systems, how can we hope to train bias out of them, double-check their decisions, or fine-tune them to behave productively and safely?

These precise concerns are what have given rise to the field of explainable AI. Mathematical techniques like LIME and SHAP can offer some intuition for why a given algorithm generated the output it did, but they accomplish this by crudely approximating the algorithm instead of actually explaining it. Mechanistic interpretability is the only approach we know of that confronts the task directly, but it has only just gotten started.

This leaves us in the discomfiting situation of relying on technologies that almost no one groks deeply, including the people creating them.

People Have Many Questions About AI

Finally, people have a lot of questions about AI, where it’s heading, and what its ultimate consequences will be. These questions can be laid out on a spectrum, with one end corresponding to relatively prosaic concerns about technological unemployment and deepfakes influencing elections, and the other corresponding to more exotic fears around superintelligent agents actively fighting with human beings for control of the planet’s future.

Obviously, we’re not going to sort all this out today. But as a contact center manager who cares about building trust and transparency, it would behoove you to understand something about these questions and have at least cursory answers prepared for them.

How do I Increase Transparency and Trust when Using AI Systems?

Now that you know why you should take trust and transparency around AI seriously, let’s talk about ways you can foster these traits in your contact center. The following sections will offer advice on crafting policies around AI use, communicating the role AI will play in your contact center, and more.

Get Clear on How You’ll Use AI

The journey to transparency begins with having a clear idea of what you’ll be using AI to accomplish. This will look different for different kinds of organizations – a contact center, for example, will probably want to use generative AI to answer questions and boost the efficiency of its agents, while a hotel might instead attempt to automate the check-in process with an AI assistant.

Each use case has different requirements and different approaches that are better suited for addressing it; crafting an AI strategy in advance will go a long to helping you figure out how you should allocate resources and prioritize different tasks.

Once you do that, you should then create documentation and a communication policy to support this effort. The documentation will make sure that current and future agents know how to use the AI platform you decide to work with, and it should address the strengths and weaknesses of AI, as well as information about when its answers should be fact-checked. It should also be kept up-to-date, reflecting any changes you make along the way.

The communication policy will help you know what to say if someone (like a customer) asks you what role AI plays in your organization.

Know Your Data

Another important thing you should keep in mind is what kind of data your model has been trained on, and how it was gathered. Remember that LLMs consume huge amounts of textual data and then learn patterns in that data they can use to create their responses. If that data contains biased information – if it tends to describe women as “nurses” and men as “doctors”, for example – that will likely end up being reflected in its final output. Reinforcement learning from human feedback and other approaches to fine-tuning can go part of the way to addressing this problem, but the best thing to do is ensure that the training data has been curated to reflect reality, not stereotypes.

For similar reasons, it’s worth knowing where the data came from. Many LLMs are trained somewhat indiscriminately, and might have even been shown corpora of pirated books or other material protected by copyright. This has only recently come to the forefront of the discussion, and OpenAI is currently being sued by several different groups for copyright infringement.

If AI ends up being an important part of the way your organization functions, the chances are good that, eventually, someone will want answers about data provenance.

Monitor Your AI Systems Continuously

Even if you take all the precautions described above, however, there is simply no substitute for creating a robust monitoring platform for your AI systems. LLMs are stochastic systems, meaning that it’s usually difficult to know for sure how they’ll respond to a given prompt. And since these models are prone to fabricating information, you’ll need to have humans at various points making sure the output is accurate and helpful.

What’s more, many machine learning algorithms are known to be affected by a phenomenon known as “model degradation”, in which their performance steadily declines over time. The only way you can check to see if this is occurring is to have a process in place to benchmark the quality of your AI’s responses.

Be Familiar with Standards and Regulations

Finally, it’s always helpful to know a little bit about the various rules and regulations that could impact the way you use AI. These tend to focus on what kind of data you can gather about clients, how you can use it, and in what form you have to disclose these facts.

The following list is not comprehensive, but it does cover some of the more important laws:

  • The General Data Protection Regulation (GDPR) is a comprehensive regulatory framework established by the European Union to dictate data handling practices. It is applicable not only to businesses based in Europe but also to any entity that processes data from EU citizens.
  • The California Consumer Protection Act (CCPA) was introduced by California to enhance individual control over personal data. It mandates clearer data collection practices by companies, requires privacy disclosures, and allows California residents to opt-out of data collection.
  • Soc II, developed by the American Institute of Certified Public Accounts, focuses on the principles of confidentiality, privacy, and security in the handling and processing of consumer data.
  • In the United Kingdom, contact centers must be aware of the Financial Conduct Authority’s new “Consumer Duty” regulations. These regulations emphasize that firms should act with integrity toward customers, avoid causing them foreseeable harm, and support customers in achieving their financial objectives. As the integration of generative AI into this regulatory landscape is still being explored, it’s an area that stakeholders need to keep an eye on.

Fostering Trust in a Changing World of AI

An important part of utilizing AI effectively is making sure you do so in a way that enhances the customer experience and works to build your brand. There’s no point in rolling out a state-of-the-art generative AI system if it undermines the trust your users have in your company, so be sure to track your data, acquaint yourself with the appropriate laws, and communicate clearly.

Another important step you can take is to work with an AI vendor who enjoys a sterling reputation for excellence and propriety. Quiq is just such a vendor, and our Conversational AI platform can bring AI to your contact center in a way that won’t come back to bite you later. Schedule a demo to see what we can do for you, today!

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