A question that exposes how much people misunderstand RAG: "How do you stop the LLM from hallucinating?"
Honest answer: you don't, fully. You layer mitigations, and none of them are bulletproof alone.
๐๐ถ๐ฟ๐๐ ๐น๐ฎ๐๐ฒ๐ฟ - ๐ต๐ฉ๐ฆ ๐ฑ๐ณ๐ฐ๐ฎ๐ฑ๐ต ๐ช๐ต๐ด๐ฆ๐ญ๐ง. You explicitly instruct the model to use only the provided context, and give it a fallback phrase for when the answer isn't there. This helps, but LLMs don't perfectly follow instructions, so it reduces hallucination, it doesn't eliminate it.
๐ฆ๐ฒ๐ฐ๐ผ๐ป๐ฑ ๐น๐ฎ๐๐ฒ๐ฟ - ๐ณ๐ฆ๐ต๐ณ๐ช๐ฆ๐ท๐ข๐ญ ๐ฒ๐ถ๐ข๐ญ๐ช๐ต๐บ. This one matters more than people think. No amount of clever prompting saves you if you retrieved the wrong chunks in the first place. ๐๐ฎ๐ฟ๐ฏ๐ฎ๐ด๐ฒ ๐ถ๐ป, ๐ด๐ฎ๐ฟ๐ฏ๐ฎ๐ด๐ฒ ๐ผ๐๐, no matter how polite your prompt is.
๐ง๐ต๐ถ๐ฟ๐ฑ ๐น๐ฎ๐๐ฒ๐ฟ - ๐ต๐ณ๐ข๐ฏ๐ด๐ฑ๐ข๐ณ๐ฆ๐ฏ๐ค๐บ. Always return the actual source chunks alongside the answer. If the model does hallucinate, the user can check the claim against the cited text immediately. This one's not really a technical fix - it's a trust mechanism. You're not preventing the lie, you're making it easy to catch.
That third layer is the one I think gets underrated. Sometimes the most valuable engineering decision isn't solving the hard problem - it's making the unsolved part visible instead of hidden.
Sharing what I'm learning in public.
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