AIRAG
Is Your AI Still Hallucinating, Even With RAG?
RAG helps, but it doesn't eliminate hallucinations entirely. Here's what to do when your AI keeps making things up.
So you implemented RAG and your AI is still making things up? Yeah, that happens. Let me explain why and what you can do about it.
Why RAG Doesn't Eliminate Hallucinations
RAG gives the AI access to your data, but it doesn't guarantee the AI will use it correctly. There are a few common reasons this happens:
The Retrieval Step Fails The AI might pull the wrong chunks of data because the question was ambiguous or the data wasn't structured well. If it retrieves irrelevant context, it'll generate an answer based on irrelevant information — or worse, fall back to making something up.
The Context Window Gets Overwhelmed If you stuff too much data into the context, the AI can lose track of what's important. It's like giving someone a 200-page document and asking them a specific question — they might miss the relevant paragraph buried on page 147.
The AI "Fills Gaps" Naturally Language models are designed to generate complete, fluent text. If there's a gap in the retrieved data, the model will try to fill it in with something plausible. That's helpful for writing essays. It's terrible for answering business questions.
What Actually Helps
Better Chunking How you split your documents matters enormously. Don't just chop at every 500 tokens. Split at natural boundaries — sections, topics, Q&A pairs. The better your chunks map to actual questions people ask, the better the retrieval.
Prompt Engineering Tell the AI explicitly: "Only answer based on the provided context. If the information isn't in the context, say you don't know." This doesn't eliminate hallucinations entirely, but it reduces them significantly.
Validation Layers Build in checks. Have the system verify that key facts in the response actually appear in the retrieved documents. If the response claims your price is $99 but no document mentions $99, flag it.
Human Review for High-Stakes Answers For anything involving medical, legal, or financial information — keep a human in the loop. AI can draft the response, but a human should approve it before it reaches the customer.
The Bottom Line
RAG is a massive improvement over raw AI, but it's not magic. The businesses that get the best results are the ones that invest in their data quality, test their systems regularly, and design appropriate guardrails. Don't just set it and forget it.