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When the stakes are high, even the tiniest, understandable mistake leads to catastrophic outcomes. For instance, a spy carefully infiltrates a top-secret facility to steal classified documents but sets off the alarm by getting a call from their mom.

Similarly, AI hallucinations might not be as harmful to someone just tinkering with GenAI out of curiosity. Sure, it makes for a scandalous tweet, like when Bing’s chatbot claimed to be head over heels for journalist Kevin Roose. But when it comes to larger enterprises, they have so much more to lose with each biased output and hallucination.

AI can become your Frankenstein

In a curious turn of events, back in May 2023, an attorney got tangled in a legal case after using ChatGPT to draft a motion. The outputs were peppered with made-up judicial opinions and legal citations. This misstep led to sanctions and a fine for the attorney.

More recently, Air Canada found itself in the legal spotlight over an AI chatbot debacle. The case revolved around accusations that the chatbot went off-script and fed incorrect details about the Bereavement Fares Policy to a customer. Despite Air Canada’s argument that the customer could have fact-checked the chatbot’s response, the court sided with the passenger, pointing out Air Canada’s lapse in ensuring the chatbot’s accuracy and taking responsibility for the information displayed on its website.

Impact of hallucinations on business outcomes

The use of AI for customer-facing enterprise solutions is meant to save time and effort. However, when AI hallucinations introduce risks into the process, they cost you way more time than they save.

One of the most obvious impacts is that it can land you in a lawsuit.

Beyond that, AI-generated hallucinations can lead to false narratives and misleading information, which can cause reputational damage to institutions. Confused and misinformed customers are a major dent in the brand’s reputation. Moreover, in sectors like healthcare and finance, where trust is currency, it also poses a great threat to your reliability and safety measures, especially in mission-critical use cases.

To err is human! (And AI)

Humans aren’t that detached from mistakes either. Alexander Pope’s words are especially true when humans are dealing with large data sets. Manually testing is a mammoth task. It is a time-consuming and resource-intensive process, covering so many possibilities. Despite all the work that goes into it, the risk of error can never be negated.

AI Self Evaluation to the rescue

Companies like OpenAI and Google made AI a household phenomenon at lightning speed with ChatGPT and Gemini. So much so that people who struggled with even online banking overnight became AI experts. While they set the benchmark for AI capabilities, they keep evolving their product to reduce the odds of hallucinations. The key to making AI more reliable and secure for your customers is AI Self Evaluation parameters.

As it is evident, to make each response more relevant, ditching manual testing means diving deeper into AI territory.

To make Conversational AI reliable and secure without the need to manually test each possibility, we have worked on the Tars Evaluation AI system. It aids in providing a relevant and accurate answer to your customers every time they ask the Chatbot a question.

It evaluates the accuracy of each question based on four parameters: Answer relevancy, Faithfulness, Contextual precision, and Contextual recall. To understand better how each parameter functions, here’s a quick guide for you.