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AI search · Practical guide

Build an evidence index for AI recommendations

A practical framework for comparing recommendations without inventing a leaderboard.

MARqentic field notes · Original research workflow

Start with the question

An answer engine can mention a product, cite a page about it, or recommend it for a particular job. Those are different observations. Build an index that stores each separately so a visible mention does not become an unsupported endorsement.

A workflow you can check

Use the following steps as a small acceptance test. Keep the underlying observations with your notes so someone else can review the conclusion.

  1. Choose a fixed set of buyer questions and record why each belongs in the sample.
  2. Record engine, question, location, retrieval time, answer text and citation URLs.
  3. Label recommendation, neutral mention and citation independently; keep ambiguous cases for review.
  4. Report counts alongside the sample size and preserve unanswered queries.

Interpret the evidence

A useful index has a transparent denominator. Compare equivalent questions across runs and describe changes as observations within your sample. Do not call a small set of prompts a market share study, or infer which provider is best from how frequently it appears.

A prompt to adapt

Replace the inputs with your own approved examples. Ask the assistant to explain its scope before starting research.

Create a recommendation evidence sheet for these buyer questions. Keep mentions, recommendations and citations separate, include source URLs and flag missing evidence.

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