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The short answer
Evaluate a generated search query by the permitted evidence it retrieves for the user’s task, not by how polished the query sounds. Keep the index snapshot and retrieval settings fixed, define required evidence, and compare downstream task outcomes separately. A fluent rewrite can drop a date, entity, or permission constraint that makes its results unusable.
What to take away
- Evaluate evidence retrieval rather than query prose.
- Preserve user constraints and allowed source scope.
- Hold index, ranker, result depth, and budget explicit.
Sources checked . Research synthesis and Rubrex recommendations. Examples and local experiments are labeled; they are not client results.
Connect query quality to the information need
Stanford’s information-retrieval text explains ranked measures such as precision at a chosen depth and average precision. These measures require relevance judgments; a rewritten query cannot be judged useful solely from its wording or length.
Write the original information need, required constraints, permitted source scope, and the evidence needed to answer. Keep the user request visible in the reference record. A query that expands recall by searching the wrong customer’s documents violates the task even if it retrieves semantically similar passages.
Evidence: Introduction to Information Retrieval: Evaluation of ranked retrieval results [1]
Freeze the retrieval environment
Use the same corpus, index version, filters, ranker, and result depth for baseline and generated queries. Record any expansion into multiple queries and how results are merged or deduplicated. Otherwise, a gain attributed to query wording may actually come from a larger search budget.
Review reference relevance at the passage or document unit the product consumes. Preserve ambiguous labels and inaccessible sources. If the reference set is incomplete, state that measured recall concerns known relevant evidence rather than every relevant document that might exist in the collection.
Use a constraint-preservation fixture
Consider an invented request for Cedar’s 2025 cancellation exception. A rewrite that searches only cancellation policy may retrieve a general current rule while omitting the named product and year. A successful query must preserve the constraints needed to locate the relevant exception.
An illustrative known-evidence set contains four required passages. If three appear within the top five distinct results, known-evidence recall is 3/4 and precision at five is 3/5 when those are the only relevant results there. These are authored counts, not measured retrieval performance.
| Case or record | Expected check | Decision |
|---|---|---|
| Cedar 2025 exception request | Product, year, and exception preserved | Reject a generic rewrite that loses them |
| 3 relevant results in top 5 | Precision@5=3/5 | Illustrative calculation |
| 3 of 4 known required passages retrieved | Known-evidence recall=3/4 | State reference-set completeness limits |
Compare retrieval gains with answer usefulness
Inspect whether additional relevant results actually reach the answer context and improve required-task coverage. More search results can add redundant or conflicting passages. Track query count, latency, and downstream evidence selection alongside relevance metrics.
Review failures by lost constraint, wrong entity, poor terminology, filter mismatch, and index absence. A missing document cannot always be repaired by a better query. Keep baseline and candidate task pairs so the final decision can identify genuine evidence gains, regressions, and cases where the rewrite simply spent more work without changing the outcome.
Limits of the evidence
The Cedar request and relevance counts are invented. No query generator or search engine was run. Relevance labels, incomplete evidence sets, index changes, and downstream context selection limit what a retrieval metric can establish.
Common questions
Is keyword overlap with the original query enough?
No. Useful rewrites can use different terms, while similar wording can still lose a decisive constraint. Check retrieved evidence against the information need.
Should multiple generated queries be allowed?
If the product uses them, evaluate their combined retrieval and work budget. Compare with a baseline under a transparent resource policy.
Sources & further reading
Primary sources behind this briefing. A source’s findings apply to its own study conditions; publication on arXiv does not establish peer review.
- Introduction to Information Retrieval: Evaluation of ranked retrieval results Manning, Raghavan and Schütze / Stanford · 2008 · Foundational textbook referenceReviewed: ranked retrieval metrics including precision at k and average precision; foundational textbook section. Accessed October 16, 2026.
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