Ranking orders candidates using signals such as lexical match, vector similarity, authority, freshness, graph distance, and access. The weights should reflect the task rather than one universal score.

Retrieval Ranking Balances Multiple Signals

Entity resolution decides whether records denote the same entity using identifiers, attributes, relationships, and uncertainty. False merges can be more damaging than temporary duplicates.

Entity Resolution Is Evidence-Weighted

A grounded answer links each material claim to evidence that actually supports it: the cited passage must support the claim's strength, scope and conditions, not merely its subject. When a source justifies only a narrower conclusion, narrow the claim or state the missing support or uncertainty. Citation presence is insufficient when a source is unrelated, contradicted, or inaccessible to the user, and knowing where a statement came from does not by itself show that it is correct.

Related guidance: [NIST AI 600-1 (July 2024), section 2.8, pp. 9-10; MS-2.5-003, p. 31](https://doi.org/10.6028/NIST.AI.600-1). This clarification operationalizes guidance; it is not an effectiveness finding.

Grounded Answers Bind Claims to Evidence

Lexical search captures exact terms, vectors capture semantic resemblance, and graph traversal captures structured relationships. Fusion should be evaluated against the task and preserve each signal's provenance.

Hybrid Retrieval Combines Lexical, Vector, and Graph Signals

In this situation—answering a current compliance question from policies and old discussions—which choice best applies “Retrieval Ranking Balances Multiple Signals”?

Learn Before

Related

Related