AI’nt That Easy #24: Guide to Eliminating AI Hallucinations in RAG Systems: Beyond Basic Prompting

Picture this: You’re reviewing a RAG implementation, feeling optimistic about its robust architecture, when suddenly you spot it — that all-too-familiar single line of code that makes every seasoned AI engineer pull a face:

prompt = "Answer truthfully using provided text only, say 'I don't know' if unsure..."

This rarely achieves the level of accuracy needed in enterprise applications. And if you think adding a single anti-hallucination line in your prompt will solve the issue, think again! Hallucinations can creep into every part of a RAG pipeline.

In this blog, we’ll dive into the nuances of hallucination and anti-hallucination in RAG systems, why each component needs to contribute to anti-hallucination, and explore some effective techniques for managing hallucination in our AI pipeline.

What is Hallucination in AI?

When LLMs generate outputs, they sometimes fabricate information that wasn’t in the training data or the context provided. This phenomenon is what we call hallucination in the AI world. And no, this isn’t some quirky AI thing — it’s a serious flaw, especially for applications requiring accuracy and reliability.

A single line in the prompt telling the model to stick to the facts won’t cut it.