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The reliable way to choose an embedding model is to test it on the queries, documents, relevance judgments, and retrieval settings your system will actually use. Run every candidate through the same frozen evaluation set, then compare ranked-result metrics such as Recall@k, MRR, and DCG or nDCG. Vector similarity is how candidates are scored; it is not evidence by itself that users will find the right result.
What an embedding-model evaluation actually measures
An embedding model converts text into vectors so that a search system can compare the meaning of a query with the meaning of documents. A typical dense-retrieval flow creates one vector for the user query and vectors for documents or passages, calculates a similarity score (cosine similarity is a common example), and ranks candidates by that score. The model is therefore one component in a larger retrieval pipeline.
An evaluation asks a more useful question than “Are these vectors similar?” It asks: When a real user submits this kind of request, does this model put the documents that satisfy the information need high enough in the result list? That distinction matters because a model can produce plausible similarity scores while still missing a critical document, over-ranking near-duplicates, or failing on a particular language, product area, or terminology style.
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Compare models as complete retrieval configurations, not as isolated vector generators. Record the model, its input conventions, preprocessing, vector similarity, index, search parameters, and any later ranking stage. If any of those change between candidates, the comparison is no longer a controlled test of the embedding model.
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Start by defining the user outcome
Choose the outcome before choosing metrics. Different applications consider different results successful:
- Find at least one useful item: common in help-center search, incident lookup, and question answering, where one relevant passage may be enough.
- Show the first useful result quickly: important when users usually click the first acceptable answer.
- Order several useful results well: important when users inspect a list, compare policies, or need multiple passages for context.
- Keep the visible list clean: important when irrelevant results damage trust or consume a small result budget.
Write down the result cutoff you will expose or pass to a downstream component. A top-five interface, a top-ten search page, and a retrieval stage that supplies 50 passages have different evaluation needs. There is no universal cutoff or universal “best” model; the appropriate choice depends on the task distribution and the cost of failure.
Build a representative evaluation set
Collect realistic queries
Use real, anonymized search requests when possible. If production data is unavailable, sample queries from the same users, content areas, and language mix that the system will serve. Include the variations that make retrieval difficult: short keyword requests, complete questions, product names, abbreviations, misspellings, references to versions, and requests that require a particular policy or procedure.
A test set made only of easy, handpicked examples will overstate quality. Include head queries and less frequent requests, and preserve the distribution that matters to your decision. You do not need to claim that a particular number of queries is universally sufficient; instead, document how the suite was sampled and why it represents the expected workload.
Freeze the document collection
Choose the exact corpus or passage collection used for the comparison. Keep document text, chunk boundaries, metadata filters, language handling, and deduplication rules constant across models. If documents are updated during the experiment, a score change can reflect corpus drift rather than model quality.
Record whether the system embeds whole documents, sections, or chunks. A model may appear better simply because a different chunking scheme gives it easier matching opportunities. Chunking, overlap, title inclusion, and field weighting belong in the evaluation record.
Create relevance judgments tied to information needs
For each test query, identify which documents satisfy the user’s need. A binary judgment can mark an item relevant or not relevant. Graded judgments can distinguish, for example, fully answers the request, partially useful, topically related but insufficient, and irrelevant. The labels must describe usefulness for that query, not merely word overlap.
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Elastic’s ranking-evaluation guidance frames the input as requests paired with ratings. Follow that structure: every query in the suite should have its own judged documents and relevance labels. If no document is relevant, record that explicitly rather than silently dropping the query; otherwise, aggregate scores can hide an important failure mode.
Document who created the labels, the labeling instructions, how disagreements were handled, and whether assessors could see the query context. For sensitive or high-stakes search, use a review process appropriate to the domain. The evaluation is only as meaningful as the judgments behind it.
Freeze the retrieval configuration
Before running candidates, create a comparison sheet with one row per model and shared settings. At minimum, capture:
- Model name and version, including the date or release identifier used.
- Exact query and document input format. Some search-oriented models expect different roles or instructions for queries and documents; follow the model’s documented convention consistently.
- Text normalization, tokenization, truncation, language handling, chunking, and metadata filters.
- Vector dimensionality and storage representation.
- Similarity function, such as cosine similarity, and any normalization applied before scoring.
- Index type, search parameters, candidate pool size, and approximate-search settings.
- Any lexical, metadata, cross-encoder, or business-rule stage applied after vector retrieval.
- Cutoffs at which results are evaluated, such as 5, 10, or the number of passages sent to another component.
OpenAI’s vector-embedding example illustrates comparing query and document embeddings with cosine similarity. Cohere’s semantic-search documentation distinguishes query and document embedding roles. Those details are operationally important: using a model with the wrong input role, or changing the similarity and index settings between runs, can invalidate the comparison.
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Choose metrics that match the outcome
| Metric or axis | What it measures | Best use | Important qualification |
|---|---|---|---|
| Recall@k | The fraction of judged-relevant documents that appear anywhere in the top k results. | Whether the retrieval set contains the material needed by a user or downstream answerer. | It does not care about the order of relevant items within the cutoff. |
| MRR | The reciprocal rank of the first relevant result, averaged over queries. | Tasks where finding one useful result quickly is the main objective. | Later relevant results do not improve a query once the first relevant result is fixed. |
| DCG | A rank-discounted total that gives more value to highly relevant documents and to documents near the top. | Lists where several results can help and relevance is graded. | The value depends on the relevance grades and the chosen cutoff. |
| nDCG | DCG normalized against the ideal ordering for the judged results. | Comparing graded ranking quality across queries with different numbers of relevant items. | Queries without an appropriate judged relevant item need an explicit handling rule. |
| Precision@k | The share of the top k results that meet the selected relevance threshold. | Keeping a visible result page or context window focused. | The result changes when you change the threshold for “relevant.” |
| Operational fit | Latency, cost, deployment constraints, language coverage, vector dimensions, and index implications. | Choosing among models after measuring retrieval quality. | These properties must be measured for your workload; no comparative values are established here. |
Use more than one metric when the product has more than one notion of success. For example, Recall@10 can show whether useful material enters a ten-item retrieval set, while MRR shows whether the first useful item is near the top. DCG or nDCG reveals whether a model orders highly useful and partially useful documents sensibly.
Elastic gives an illustrative interpretation of Recall@10: if a query has eight judged-relevant documents and four appear in the top ten, Recall@10 is 0.5. This is a definition example, not a benchmark result or a performance expectation for any model.
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Do not infer quality from the raw similarity score. Scores are produced by the retrieval mechanism and are often not comparable across models, dimensions, or preprocessing choices. Evaluate the ranked documents against judgments instead.
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- Write the decision rule. State the primary outcome, cutoffs, primary metric, secondary metrics, and operational constraints. Decide in advance how you will handle ties, queries with no judged relevant document, and models that fail an operational requirement.
- Prepare one frozen test package. Store the query set, corpus snapshot, relevance labels, preprocessing configuration, and retrieval settings under identifiable versions.
- Encode each candidate consistently. Apply the documented query and document roles, handling truncation and errors the same way. Log failed requests rather than silently removing them.
- Build equivalent indexes. Use the same corpus, chunk IDs, filters, similarity function, search cutoff, and post-retrieval stages. If a model requires a different dimensional index, record that difference while keeping the retrieval procedure otherwise equivalent.
- Retrieve results for every query. Save ranked document IDs and scores at every cutoff you plan to report. Keeping the complete ranked lists makes later error analysis possible.
- Compute metrics from the judgments. Calculate Recall@k, MRR, DCG or nDCG, and Precision@k as appropriate. Report the aggregation rule and the number of evaluated queries.
- Inspect per-query results. Compare scores by query and by slice, not only one overall average. A model that wins overall may fail badly on a small but important class of requests.
- Review disagreements and failures. Read examples where candidates differ, where all candidates fail, and where a model retrieves a plausible but non-answering document. Correct labeling errors before drawing conclusions.
- Measure operational behavior separately. Under a stable workload, record latency, throughput, resource use, storage impact, and recurring inference or hosting cost if those factors affect deployment.
- Select or iterate transparently. Choose the model that best satisfies the written decision rule, or explain the trade-off when no candidate dominates. Keep the evaluation package so a later model can be tested against the same baseline.
Analyze results by query type, not just the average
An aggregate metric compresses many behaviors into one number. Create slices that reflect real product risks, such as short versus long queries, exact identifiers versus natural-language questions, languages, departments, document types, freshness bands, or queries requiring one document versus several.
For each slice, show the number of queries and the same metrics used for the overall comparison. A model that improves broad topical matching but loses exact policy lookups may be unsuitable even if its overall nDCG rises. Conversely, a small regression on rare exploratory queries may be acceptable if the model materially improves the high-volume task that defines the product.
Keep a failure log with the query, expected information need, top results from each candidate, relevance labels, and a short diagnosis. Useful diagnoses include missing terminology, over-weighted generic language, confusion between versions, poor handling of names or codes, duplicate results, and a relevant passage that was never included in the candidate set. This log guides data, chunking, filtering, and reranking fixes; it prevents every problem from being blamed on the embedding model.
Separate retrieval quality from answer quality
If retrieved passages feed an answer-generating system, retrieval evaluation is necessary but not sufficient. First determine whether the needed evidence entered the retrieved set. Then assess whether the downstream answer uses that evidence correctly and remains useful to the user.
The materials supporting this method establish ranked-result evaluation, not a complete end-to-end protocol for generated answers. Do not present Recall@k, MRR, or nDCG as proof that an answer is factually correct. Add a separate answer-quality assessment suited to your application, with its own criteria and reviewed examples, while keeping retrieval metrics available to diagnose whether a failure began at retrieval or generation.
Account for practical deployment constraints
Once relevance quality is measured, compare operational fit. Dimensions and index requirements affect memory and storage. Encoding throughput affects ingestion time and the ability to keep a changing corpus current. Query latency and concurrency affect the user experience. Language and domain behavior affect coverage. Cost and deployment options affect whether a model can run within the product’s constraints.
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Measure these properties with the same corpus size, hardware class, request mix, and concurrency assumptions you expect in production. Report the conditions alongside every number. A latency measured for a warm local index is not interchangeable with a value measured through a remote service, and a one-time indexing cost is not the same as recurring query cost.
Operational fit should not override a material relevance failure without an explicit product decision. If a lower-quality model is chosen for cost or latency, record which user outcome is being traded away and monitor that outcome after deployment.
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Testing only handpicked successes
Symptom: Every candidate appears strong, but users report misses after launch.
Fix: Build the suite from representative requests, include difficult and unsuccessful cases, and preserve the expected query distribution.
Changing several variables at once
Symptom: A score improvement is attributed to the model even though chunking, filters, similarity, or reranking also changed.
Fix: Freeze the retrieval pipeline and document every exception. Run a separate experiment when testing a pipeline change.
Using similarity scores as quality labels
Symptom: A model with larger or more widely distributed scores is declared better.
Fix: Compare ranked document IDs with query-specific relevance judgments. Raw scores are not a substitute for judged outcomes.
Reporting one average
Symptom: A small set of high-volume or easy queries hides failures in important slices.
Fix: Report per-query or slice-level results, inspect disagreements, and explain how the aggregate was calculated.
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Using binary labels when usefulness is graded
Symptom: A merely related result receives the same credit as an answer that fully resolves the request.
Fix: Use graded judgments and DCG or nDCG when different degrees of usefulness matter. Define the grades before labeling.
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Dropping queries with no relevant document
Symptom: The evaluation looks artificially strong because impossible or out-of-scope requests disappeared.
Fix: Keep those queries, define how each metric handles them, and report their count. They may indicate corpus gaps rather than model weakness.
Confusing retrieval with downstream answers
Symptom: A correct retrieval metric is treated as evidence that generated answers are correct.
Fix: Evaluate retrieval and answer usefulness as separate stages, then connect failures through shared examples.
What a defensible evaluation report contains
A reader should be able to understand exactly what was compared and reproduce the decision. Include:
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- The user outcome, primary metric, secondary metrics, cutoffs, and decision rule.
- How queries were sampled, how many were evaluated, and which slices were used.
- The corpus snapshot, document or chunk definition, metadata filters, and relevance-labeling instructions.
- Each model’s version, query/document input convention, dimensions, preprocessing, and error handling.
- Similarity function, index type, search parameters, candidate pool, reranking, and any other ranking stage.
- Aggregate and per-slice metrics, with the relevance-grade definitions used for each metric.
- Representative failure cases and the actions they suggest, such as better labels, chunking changes, filters, or reranking.
- Latency, cost, storage, language, and deployment measurements with their workload and environment.
- Known limitations, including missing query types, uncertain labels, corpus drift, and any untested production condition.
The cited guidance establishes these comparison components but does not prescribe one universal query count, significance test, or winning model. State the method your team selected and why it matches the decision stakes.
A practical example of the reasoning
Suppose an internal support search must return passages for two uses: a user-facing list of five results and a downstream component that can consume ten passages. Define separate cutoffs for those uses. Label each query’s passages as fully answering, partially useful, related but insufficient, or irrelevant. Use Precision@5 and MRR to understand the visible list, Recall@10 to test whether the evidence enters the downstream set, and nDCG@10 to evaluate the ordering of graded usefulness.
Run every candidate with the same support corpus, chunks, filters, vector similarity, and post-retrieval logic. If one model has higher Recall@10 but lower MRR, it is finding more of the needed evidence while placing the first useful passage lower. The decision then depends on whether the downstream component benefits more from completeness or whether users mainly need one answer immediately. That is a product decision made visible by the metrics, not a conclusion that one metric is universally superior.
Make the decision and keep the baseline
Choose the model that satisfies the stated user outcome and operational constraints on the representative suite. If candidates trade strengths, make the trade explicit rather than hiding it behind a single score. Preserve the corpus snapshot, labels, configuration, ranked outputs, and analysis so future model versions can be evaluated without changing the yardstick.
After deployment, continue sampling real queries for new judgments. Content changes, new terminology, and user behavior can alter the distribution that made the original winner preferable. A repeatable evaluation package turns model replacement from a guess into a controlled comparison.
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