When AI lies: Detecting hallucinations in Gen AI

Have you ever asked generative AI a straightforward question, only to receive a wildly inaccurate or even downright bizarre answer?

Maybe you requested a text summary, but the AI confidently fabricated details that weren't in the original text. Or maybe you asked for technical advice, only to receive instructions that were completely wrong.

These errors—known as "hallucinations"—happen when AI generates information that isn't grounded in its training data.

Hallucinations aren't always bad. In creative tasks, they can be a feature, not a bug—improvising fictional characters or dreaming up imaginative stories. But in high-stakes areas like healthcare, finance, or legal services, hallucinations can be dangerous—sometimes even life-threatening.

As Generative AI becomes more embedded in application development, it's crucial to understand why hallucinations happen—and how to manage them effective.

In today's newsletter, we'll cover:

• What makes LLMs hallucinate in the first place

• Why detecting and controlling hallucinations matters

• How AWS tools can help you detect and manage AI hallucinations

Let's dive in.

What makes LLMs hallucinate?

To understand hallucinations, it's important to first understand how large language models (LLMs) generate content. Let's use an example: text generation.