Definition
Retrieval Evaluation is the process of measuring the quality of the sources and passages an AI system retrieves before or while generating an answer. It asks whether the model found the right evidence, not just whether the final answer sounded good.
In GEO, retrieval evaluation helps teams diagnose visibility problems. If a brand is absent from AI answers, the issue may be crawl access, weak content, poor entity signals, stale indexes, competitor authority, or the model retrieving the wrong passage.
Useful retrieval metrics include source recall, citation precision, passage relevance, freshness, source diversity, answer coverage, and whether key owned pages appear in the retrieved set. Teams can evaluate retrieval manually through prompt testing or programmatically when APIs expose sources.
Retrieval evaluation is especially important for RAG, AI search, documentation, ecommerce, and regulated topics where the quality of evidence directly affects trust.
Examples of Retrieval Evaluation
- A documentation team tests whether AI coding assistants retrieve the current API page instead of an outdated Stack Overflow answer.
- A GEO analyst scores retrieved passages for relevance before deciding whether a missing citation is a content problem or an indexing problem.
- An ecommerce team evaluates whether AI shopping tools retrieve canonical product pages, reviews, and current inventory data.
- A compliance team checks retrieval sources for financial advice prompts before approving AI-generated answer summaries.
