Fact Retrieval
Also called Fact Retrieval
Pulling a specific, checkable fact on demand instead of trusting what the model half-remembers.
Think of it like
Looking up someone’s exact phone number instead of reciting the digits you think you memorized.
Example
Asked "what’s our current refund window?", the system fetches the live policy value rather than letting the model guess from training data.
How it actually works
Models store facts fuzzily in their weights, which is why they confuse dates and invent figures. Fact retrieval treats precise facts as a lookup problem: fetch the exact value from a trusted source and put it in front of the model. It’s the antidote to a knowledge cutoff and a big lever against hallucination — but only as reliable as the source you retrieve from.
For product teams
Turns "the model sometimes gets our numbers wrong" into "the model reads our numbers off the record."
For engineers
Resolve factual queries against an authoritative store and inject exact values into the prompt rather than relying on parametric memory.
Related
- Hallucination — The failure mode it fights.
- Knowledge cutoff — Why the model’s own facts go stale.
- RAG — The general pattern of reading before answering.
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