How to navigate the 3 risks of LLMs in CAI

How to navigate the 3 risks of LLMs in CAI 1920 1080 Ben McCulloch

Most companies want to know how to find success with generative AI today. Few have managed to demonstrate it. Loop Car Insurance is one company that’s getting it right.

It launched a generative, LLM-based bot with the help of Quiq last year that has reduced email support by 55%. So how did it do it?

Kat Garcia, Director of Member Services at LOOP Car Insurance, and Mike Myer, CEO, Quiq, joined Kane Simms in a recent webinar, to discuss the behind the scenes factors in what it takes to get a generative AI chatbot to production in 2024.

Essentially, success comes from mitigating the 3 biggest risks in generative AI for enterprise: presenting the wrong advice, handling privacy and protecting your brand image.

Risk #1 – Giving the wrong advice

LOOP provides car insurance. It needs to be absolutely sure it’s providing correct information at all times.

Being ‘correct’ is contextual of course. LOOP works within a specific sector of the insurance industry (providing it to car owners), they work within a specific geographical area (Texas, America), and it has its own products and services. This means that the information it provides must be specifically tailored to its use cases.

So how do you achieve that?

You train the LLM on your documents and data. You ensure that your knowledge base is up to date, doesn’t contain contradictions (by following a ‘single source of truth’ approach), and it should only contain documents that you know to be relevant (that list of cake recipes you keep on your hard drive should be kept well clear of the training data).

As Mike says, when Quiq implemented the LLM for LOOP, it needed to make sure “the answer is using the information that was provided by LOOP and no other information.”

Quiq also implemented a novel approach to prompt chaining and guardrails that ensures the Loop agent doesn’t go off course or hallucinate, and fails gracefully if it can’t answer a specific question.

Risk #2 – treat your customer’s data with care

You don’t just need to be careful about the materials that go into the bot when you design and train it. In every conversation, the customer could provide PII. There’s a very good chance your bot will directly ask the customer for their data, as a means of identifying them and providing a helpful service.

You need to treat that data with respect and care. Whose servers will it end up on? Are you sure that every third-party data handler involved with your bot has suitable processes for dealing with that data? Are you definitely using models that won’t use your data as training data? Are you scrubbing PII data from your interaction logs?

LLMs are new to most people, and there are a lot of startups offering them. Make sure you do your due diligence and only use models that are fit for enterprise consumption.

Quiq chose to use OpenAI’s GPT 3.5 model via Microsoft Azure for this deployment and the platform being SOC 2 compliant took care of any other concerns.

Risk #3 – Is the bot doing damage to your brand?

Consider this: the bot might be the first contact a customer makes with a brand. It’s generating responses on the fly that will affect whether the relationship continues or not. Your bot might be a make or break interaction that either wins or loses a customer. The stakes are that high!

As Mike says, “we want to make sure that the answer that gets provided is on the brand tone, and is not something that somebody is going to screenshot and post on social media somewhere.

We’ve recently seen this happen a few times. Chevrolet was caught out, as was DPD. Even Air Canada was publicly shamed for its ‘AI’ mishap, however the incident occurred before the widespread use of LLMs, and so it was most likely caused by an old-school NLU-based bot that hadn’t been updated properly. All the Air Canada example shows is that people will jump to their own conclusions about how you made a mistake, but it’s the mistake that they will remember!

When using generative AI to create a bot’s utterances you can define the personality. It’s best practice. You want the responses to be consistent within the experience, you want them to be contextually relevant to the experience (for example, a bot helping people in a hurry shouldn’t be verbose), and you want them to represent the brand.

Although Quiq didn’t go as far as developing a fully fledged ‘personality’ document, it gave instructions on how to respond and made sure the agent is conscientious, empathetic and has a more casual tone.

The end result

Since going live in 2023, LOOP experienced a 3x improvement in resolution rate compared to its previous assistant. Its LLM-based bot is answering 50% of customer questions, 24 hours per day, all year long (whereas its call centre is open 8 hours a day). It also measured customer satisfaction and found that the bot was on par with their human agents. Finally, it has managed to help reduce email support by 55%, saving human agents time.

This is the perfect example of a use case that’s right for generative AI, as well as a technology choice and implementation approach that fully understands and mitigates the risks involved. A great model for anyone wanting to do enterprise generative AI properly.

Watch the full webinar and learn the real details behind this deployment.

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