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Repair Windows errors before they cause bigger problemsFix Now →Fix the driver behind crashes, sound loss and screen glitchesFind Drivers →Clear out junk files and repair common Windows errorsFree Scan →Before shipping a ranking change, compare it with the current ranking on a representative set of real queries that people have judged for relevance. Use the same judgments and task-appropriate metrics for both versions, then inspect individual query wins and losses—not just the overall score. Offline evaluation makes comparisons repeatable; if the decision depends on how live users respond, follow it with a controlled online experiment.
Build a test set that represents real search needs
Start with queries that reflect the range of work your search system performs. Include common searches, queries important to the business, and queries the current ranking handles poorly. Also include query types beyond the one that prompted the proposed change: a model or ranking adjustment can affect many searches, not only the motivating example.
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Elastic recommends evaluating a representative suite of typical user queries, while Microsoft’s guidance for SharePoint ranking-model tuning calls for testing popular, business-important, and poorly handled queries. The principle applies broadly, though Microsoft’s workflow is specific to SharePoint. Elastic’s ranking evaluation documentation and Microsoft’s SharePoint ranking-model guidance explain these approaches.
Make the test set useful, not merely large
- Cover different query types and information needs, not just high-volume terms.
- Include searches where errors would have meaningful user or business consequences.
- Retain difficult queries so you can tell whether the change improves or worsens known weak spots.
- Record the test queries and use the same set when comparing the baseline and candidate.
The reviewed guidance does not establish a universal minimum number of queries. Choose coverage based on your query distribution, the risks of the change, and how much evidence you need to make a decision.
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Create relevance judgments for query-result pairs
A relevance judgment says how well a particular document satisfies the information need behind a particular query. The same document may be relevant for one query and irrelevant for another, so judgments must be tied to query-document pairs. Elastic explicitly notes that ratings are needed for each test query and should reflect the information need of the person who entered it. See Elastic’s evaluation documentation.
Choose binary or graded labels
- Binary labels (relevant or not relevant) work when the distinction is clear and the task does not require finer ranking preferences.
- Graded labels distinguish degrees of usefulness—for example, an excellent answer, an acceptable result, and a poor match. They are more informative when several documents can satisfy the same need to different extents.
Write the labeling rubric before reviewers assess results. Define what counts as relevant for each kind of query, and make sure reviewers use the query’s intended meaning rather than personal preference. The Elasticsearch Labs guide to search-quality evaluation with judgment lists discusses judgment-set design and evaluation.
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Check disagreement, bias, and coverage
Reviewers can disagree, and people may favor results shown near the top simply because they appear first. Where practical, reduce position bias—for example, by presenting results without revealing their ranking position—and make disagreements visible rather than treating every label as unquestionable ground truth. Click data can help at scale, but clicks are influenced by position, presentation, and the amount of data available; a click is not automatically proof of relevance.
After running the candidate, check whether its top results have judgments. A ranking change may bring previously unseen documents into prominent positions. If those documents are unrated, the metric may describe only part of the candidate’s result set. Add judgments for new top results before trusting the comparison. Microsoft’s ranking-model tuning guidance covers judgment coverage in its SharePoint workflow.
Select metrics that match the search task
No single metric answers every relevance question. Choose the measure based on the failure you most need to detect, then hold the metric, relevance labels, and evaluation cutoff constant for the baseline and candidate. A cutoff is the depth of results being evaluated—for example, the first several results users are likely to see.
| Metric | What it measures | Useful when |
|---|---|---|
| Precision | The share of returned results judged relevant. | Irrelevant results in the displayed set are costly. |
| Recall | The share of relevant documents retrieved within the evaluated set. | Missing useful documents is a key failure mode. |
| Mean reciprocal rank (MRR) | How highly the first relevant result appears. | People usually need one strong answer and a fast first hit matters. |
| DCG or nDCG | Graded relevance and result order, giving greater weight to stronger results near the top. | Multiple results can satisfy a query at different levels of quality. |
| Click-through or conversion measures | Observed interaction or downstream behavior. | As online signals alongside relevance judgments, with care around position, presentation, and sample size. |
Elastic’s evaluation material documents precision, recall, MRR, and DCG/nDCG; the use cases above follow from what those metrics measure. For metric definitions and evaluation options, consult Elastic’s ranking evaluation documentation and the judgment-list evaluation guide.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.Compare the candidate with the baseline
Run both ranking versions against the same judged queries and document set, using identical settings for metrics and cutoffs. Compare aggregate results to see the overall direction, then investigate the query-level changes. An aggregate score can conceal a severe regression on an important query or a whole class of searches.
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- Freeze the evaluation inputs. Record the query set, judgments, metric definitions, and result cutoff used for this comparison.
- Evaluate the current ranking and candidate. Keep the evaluation conditions consistent so differences are attributable to the ranking change rather than a changed test.
- Review overall movement. Note whether the candidate improves or worsens the selected metrics across the suite.
- Inspect query-level gains and losses. Prioritize important queries and segments, and look for patterns across query types.
- Verify judgment coverage. Check whether new high-ranking candidate documents are rated; add judgments where needed before drawing conclusions.
- Inspect the results behind meaningful changes. Determine whether the movement reflects genuinely better matches, changed ordering, or incomplete judgments.
Keep the query-level evidence with the aggregate score. A higher average is not, by itself, a sufficient reason to ship if it masks a harmful regression that matters to users.
Use an online experiment to test live impact
Offline evaluation is useful for repeatable iteration: it lets you test candidate rankings against a fixed set of queries and judgments before exposing a change broadly. It cannot, on its own, establish that live users will behave better. If the ship decision hinges on real-world outcomes, compare a control with a treatment using an A/B test or an appropriate interleaving design. Clicks and business outcomes can add evidence, but they complement rather than replace relevance judgments.
The Elasticsearch Labs evaluation guide discusses offline and online evaluation, and Elastic’s guide to the Ranking Evaluation API describes evaluation as part of search-quality testing. Neither method creates a universal experiment duration or sample-size rule; those choices depend on traffic, query distribution, expected effects, and the consequences of a bad change.
Ship with monitoring and a rollback plan
For a rollout, decide in advance what relevance and operational signals you will watch, who can pause the change, and how to restore the prior ranking if needed. Track search quality after release as well as operational behavior. Latency or query duration is not a relevance metric, but slow search can still undermine the practical quality of the experience.
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The reviewed guidance supports monitoring and experimentation as quality practices; it does not prescribe a universal rollout duration, sample size, or rollback threshold. Set those locally based on your traffic, metric variability, and the risk of the proposed change rather than treating an arbitrary score or observation window as a general ship rule.
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