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What is Retrieval Grounding

Definition

Retrieval grounding is the technique of anchoring a language model's responses in retrieved source documents, supplying relevant evidence as context so the model's answers are based on actual data and can be traced back to sources rather than generated from memory alone.
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  • Anchors AI answers in real retrieved evidence
  • Reduces hallucination by grounding responses in sources
  • Enables traceable, citable answers back to documents
  • Keeps responses current with the retrieved knowledge base

Real World Example

A support assistant uses retrieval grounding to base each answer on the specific help articles it retrieves, citing them, so responses reflect the actual documentation rather than the model's unverified recollection.

FAQs

What is retrieval grounding?

Anchoring model responses in retrieved source documents so answers are based on real evidence and traceable to sources.

How does it reduce hallucination?

By giving the model relevant source context, it bases answers on actual data rather than inventing them.

How does it relate to RAG?

Retrieval grounding is the core principle that makes retrieval-augmented generation effective.

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