In today’s modern world, customers are becoming increasingly used to dealing with AI. From initiating a purchase return from a brief interaction with a chatbot, to scheduling a doctor’s appointment over the phone with a voicebot, conversational AI has become an integral part of many industries’ day-to-day customer interactions.
Cyara helps businesses transform their chatbot development with our conversational AI optimization solution.
While conversational AI-driven tools can provide cost-efficient and effective customer experience (CX), there’s no denying the many risks that lie just around the corner. Large Language Models (LLMs), for example, can generate data that may expose your brand to a wide range of serious reputational and compliance risks if not tested properly.
An Example of an LLM-Related Risk
Imagine that you’re the owner of a retail business that sells clothing. In the past, your customer service agents have been bogged down in requests to return items, questions regarding item sizing, and more. However, these requests are simple and easily managed by your LLM-driven chatbots, allowing your agents to focus on more complex customer queries and reallocate resources for better overall organizational efficiency.
However, when a customer tries to ask a simply question about an item’s color, they are shocked when your bot begins to use inappropriate language. During the interaction, the bot begins to curse at your customer, going as far as to criticize your own company. In this example, your business will potentially lose out on a customer, and they may post negative feedback based on their experience that will drive other future sales away as well—all because of an easily-avoidable bot-related error!
And, compared to other possibilities, this is a minor risk that may only have small repercussions for your business’ long-term success. Real-life cases of bot misuse have emerged over the past several years, where this type of technology has been used to impersonate political figures, spread misinformation, compromise private information, or worse…
Testing Your LLM-Powered Bot to Prevent Risks
Throughout your chatbot development lifecycle, it’s critical to continuously test and monitor performance to assure that your product is prepared to perform at scale, with a minimal risk of suffering a defect. At the end of the day, it’s important to ensure your bot meets your quality standards before it enters production and your customers are exposed.
While each LLM-powered bot will serve a different purpose depending on your brand’s specific needs, here are a few testing types that you should use during development:
Functional Testing
Generally speaking, functional tests verify whether your software is performing as designed. For LLM-powered bots, that means evaluating whether or not your bot is capable of responding to customer requests with accurate and reliable information. By leveraging continuous functional tests, you can confirm that your design works as intended and that your bot is equipped to handle necessary tasks to provide quality CX to your customers.
Security Testing
Security testing is critical for any bot that is tasked with handling sensitive information, such as patient information for a healthcare provider, or a bot that gathers payment information for a retail business. If your bot isn’t properly tested, your customer data can easily become compromised, resulting in severe compliance and reputational penalties for your enterprise.
Security tests are a must to ensure that your bot follows legal requirements, compliance standards, and more.
Regression Testing
Regression tests are essential whenever you update your LLM-powered bot or change your infrastructure to confirm that it still works as intended. Software is constantly changing, and it’s important to verify that any updates aren’t negatively impacting your bot’s ability to support your customers.
For example, you may want to roll out an update to your chatbot that allows a customer to schedule regular payments. However, to add this new feature, you must retrain it to understand these new requests and respond appropriately. During the process of updating your bot, a part of the code may be accidentally edited or dropped, leaving your bot unable to perform tasks it previously was able to complete without a problem. You can identify any defects that were caused and take proactive steps to remedy the issue by conducting thorough and frequent regression tests.
In these instances, you can be confident that your update was successful and didn’t create new issues that will appear further down the line.
Humanification Testing
Humans express their queries in many ways, and it’s important for your LLM-powered bot to understand human intent. Without this understanding, it will be extremely difficult for your bots to handle common customer issues and answer questions.
For example, with humanification testing, you can train your bot to respond to a customer query correctly, even when the request contains elements such as emojis, typos, slang, and more. By understanding the way people express themself in type, you can ensure that your bot can provide a seamless interaction and respond appropriately.
Assure Quality at Scale with Cyara
LLM-powered bots are extremely complex and require rigorous testing and monitoring to ensure that they perform at scale. With traditional, manual processes, it can be an extreme drain on your team’s time and resources.
But you don’t have to manage the testing alone. Cyara’s AI-driven, CX Transformation Platform helps you optimize your LLM-powered bots throughout every stage of development and assure quality at scale. Contact us to learn how you can accelerate development and deliver flawless CX across all your AI-led, omnichannel journeys.
