liveBook · Manning

Relevance

As explained in Build an AI Agent (From Scratch)

Relevance is the degree to which an answer focuses on the central aspects of a user's question without unrelated content.

Answer relevancy measures whether an agent directly addresses the core intent of a user’s question rather than evading, answering a different question, or replacing the answer with background. section 10.2.4 section 10.3.2

Apply answer relevancy as a binary rubric item:

  • PASS when the response directly answers the user’s core question and provides a clear takeaway or conclusion. section 10.2.4 section 10.3.2
  • FAIL when the response gives only a plausible summary, answers another question, reframes the request, rambles with background, or lacks a conclusion. section 10.2.4 section 10.3.2
  • Prefer PASS/FAIL decisions when consistency matters; graded scales require harder-to-stabilize distinctions between adjacent scores. section 10.2.4

A rubric-based evaluator receives the user query, final answer, rubric, and, when available, an expected answer. For research agents, it can also receive an evidence pack containing source identifiers, metadata, and relevant excerpts. The evaluator must judge only the supplied evidence, avoid rewarding verbosity or friendliness, and return one JSON object. Track the answer relevancy rate as the fraction of evaluations passing this rubric item; combine it with citation reliability and requirement compliance in an overall pass rate. section 10.3.2 section 10.2.4

How Build an AI Agent (From Scratch) covers it

How deep it goes
Defines binary PASS/FAIL criteria and embeds them in a rubric-based evaluator, without using partial credit for this item.
Examples
Uses user prompts, final answers, expected answers, evidence excerpts, and PASS/FAIL evaluation outputs as assessment artifacts.
What it assumes
Assumes a user query and candidate final answer, then establishes explicit criteria for judging directness and completeness.
What you can do after
Evaluate agent answers with a binary rubric and track answer relevancy rates alongside other quality indicators.
How it connects
Connects answer relevancy to rubrics, metrics, citation reliability, requirement compliance, and evidence packs.

Also explained in

Compare how 11 books explain relevance →

Related concepts

This explainer was generated by Manning's knowledge graph from the text of Build an AI Agent (From Scratch). The section links point to the parts of the book it draws on.