AI / A CONCEPT NOTE
Grounding
anchoring answers in verified facts
Overview · mechanism
pitfall · examples
01 / THE SHORT VERSION
The idea in a few sentences.
Ensuring model responses are based on verifiable, reference facts or documents (like your own documentation in RAG) rather than its internal parametric training data.
02 / FOLLOW THE MECHANISM
How fact anchoring works
Data match
app pulls verified documentation from repository index database.
Injection
places documentation text block in LLM prompt system context.
Generation constraints
directs model: 'Answer only based on the facts provided below.'
Reference output
model outputs answer citing exact lines from source documents.
04 / COMMAND NOTES
Read the command, then the result.
Inspect the flags and arguments before trying an example. Snippets can need local setup, replacement values, or resources in your own environment.
test grounding accuracy
promptfoo eval -c RAG-grounding.yaml05 / CHECK YOURSELF
Could you explain Grounding to a teammate?
Try it out loud in two sentences: what it is, and the one detail that changes the picture. If you stall, the gap is the part to reread.
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