Every time my chatbot makes something up, I look at the source documents and realize the retrieval failed. Why are RAG systems still so prone to fetching the wrong info? Is it a fundamental flaw in the architecture or just poor implementation by the developers?
3 answers
Hallucination is often a symptom of "context poisoning." If your RAG system retrieves three correct documents and two irrelevant ones, the LLM might try to merge them into a single, nonsensical answer. This is a design flaw in the filtering stage. To prevent this, you should implement a "threshold" for similarity scores. If a retrieved document doesn't meet a certain confidence level, it shouldn't be sent to the LLM at all. Most systems are built to always return "k" results, even if those results are garbage. That is what leads to the most common errors.
Do you think adding a verification step where the LLM checks the answer against the retrieved snippets could reduce these hallucinations?
Most people just need better prompts. Telling the AI "don't answer if you don't find it in the text" solves a lot of these problems.
Prompt engineering is a huge part of it, Rebecca. A strict system instruction can act as a final guardrail against those retrieval errors.
Scott, that is called "Self-RAG." It definitely helps, but it increases costs and latency because you are making multiple LLM calls. For many businesses, the trade-off in speed isn't worth the extra accuracy, which is why they settle for simpler, "worse" designs.