Video / Short
RAG Reduces AI Hallucinations
RAG can reduce AI hallucinations by grounding answers in retrieved information, but unclear or conflicting sources still require human verification.
Video summary
This short explains that retrieval-augmented generation, or RAG, is intended to ground an LLM in a supplied folder, database or other set of information. Even so, the speaker says a system may still hallucinate because language models tend to provide an answer even when the relevant information is absent.
They suggest using programmatic rules that constrain the answers being created, while warning that this can create a long implementation tail. RAG is presented as a way to reduce hallucinations, not a guarantee that every response will be correct.
Video transcript
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One of the problems with running RAG, or retrieval-augmented generation, is that it will sometimes hallucinate. Now, it shouldn't do that because RAG is designed to only have access to the data that you've put aside, whether it's a folder, a database, or whatever. LLMs like to please. They like to give answers even if they don't have the correct information, and that's called a hallucination. You can get around that by programmatically insisting on rules for the answers that are created, and that can overcome it. But that can be a long tail to the implementation of RAG.