Retrieval Relevance
Retrieval relevance tells how well a search result or answer meets the user's stated need under clear scoring rules.
Another example
A search reviewer checks 100 questions and gives each found passage a grade. The team measures ranking at set result counts and by question type. A vector similarity number alone is not a relevance judgment.
Main parts
- 01information need
- 02candidate item
- 03graded judgment
Use it when
Use this term when judging whether search results or an answer meet a clear information need.
Do not use it when
Do not treat relevance as the same as correctness, popularity, similarity, or business value.
Name used in code
query × item → graded judgment Before you ship
Check the model and prompt versions, source data, versioned evaluation set, and measures. Verify links to evidence, tool permissions, privacy, common failures, decline-to-answer and fallback behavior, human review, monitoring, cost, speed, and rollback.
Outcome: Use or evaluate Retrieval Relevance to make AI behavior measurable and tied to evidence. User context: A search reviewer checks 100 questions and gives each found passage a grade. The team measures ranking at set result counts and by question type. A vector similarity number alone is not a relevance judgment. AI method or concept: Retrieval Relevance. Why it fits: Use this term when judging whether search results or an answer meet a clear information need. Do not use it when: Do not treat relevance as the same as correctness, popularity, similarity, or business value. AI requirements: Define the input and source evidence. Set model and tool permissions. Use versioned evaluation data and measures. Define failure, decline-to-answer, privacy, speed, and cost limits. Operational safeguards: Keep a clear trace and hide sensitive log data. Show users a safe fallback. Mark steps that need human review. Define how to roll back the model or prompt. Acceptance criteria: Record baseline and target measures on a versioned evaluation set. Test edge cases and hostile inputs. Verify fallback, monitoring, permissions, and rollback. Evidence and limits (evidence boundary): No single official standard controls the whole term. Its meaning may change across teams, platforms, or frameworks. Unknowns to confirm: Target task, model and version, evaluation owner, source data, risk limit, tool permissions, and production fallback.
Check this request
Review the current use of Retrieval Relevance. Definition: Retrieval relevance tells how well a search result or answer meets the user's stated need under clear scoring rules. Release checks: Check the model and prompt versions, source data, versioned evaluation set, and measures. Verify links to evidence, tool permissions, privacy, common failures, decline-to-answer and fallback behavior, human review, monitoring, cost, speed, and rollback. Before changing code, report the evidence you found, gaps, severity, and the smallest safe fix.