The reliable way to evaluate an embedding model is to test it on the queries and documents your system actually serves, using relevance judgments that represent users’ information needs. Encode each candidate with the same corpus, chunking, vector index and search settings; rank the results; then compare task-appropriate measures such as Recall@k, MRR and nDCG. A cosine-similarity score by itself is not evidence that search is useful.
What you are evaluating
An embedding model converts text into vectors so that semantically related queries and documents can be compared in a numeric space. A typical search flow creates a vector for a user query, creates vectors for documents or passages, compares the query vector with candidate vectors (cosine similarity is one common choice), and returns the highest-scoring items. OpenAI’s vector-embedding documentation describes this query/document comparison pattern, while Cohere’s semantic-search guidance distinguishes the roles of query and document inputs.
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The model is only one part of that pipeline. Chunk size, text cleaning, language handling, metadata filters, vector index settings, approximate-nearest-neighbor parameters, score thresholds and later ranking stages can all change the displayed results. Therefore, evaluate the ranked result list produced by the complete retrieval setup, not the embedding vectors in isolation.
- Unit of evaluation: the ranked documents or passages returned for a query.
- Evidence required: a representative query set, a fixed document collection and relevance judgments for every test query.
- Comparison rule: every candidate model must run under the same conditions unless the experiment explicitly measures a configuration change.
- Decision output: a quality result for the user task, plus operational facts such as latency, cost, language coverage, vector dimensions and index implications.
Start with the user outcome
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Finding any useful result
If the application needs at least one relevant passage in a context window, retrieval completeness at a fixed cutoff is the priority. Recall@k is usually the clearest primary measure, where k equals the number of passages you can retrieve or pass to a later stage.
Putting the first useful result first
For a search page or support workflow in which users act on the first good result, use Mean Reciprocal Rank (MRR). It rewards a relevant result at rank one more than the same result at rank five.
Ordering several results by usefulness
When multiple results can help and labels distinguish “highly relevant” from “partly relevant,” use DCG or normalized DCG (nDCG). These measures reward high relevance and early placement together.
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Keeping the visible list clean
Precision@k is useful when showing irrelevant results is especially damaging. It reports how much of the first k results meets your chosen relevance threshold.
You can report several metrics, but designate one primary metric tied to the product decision. Otherwise a model can appear to win simply because the comparison emphasizes a metric that does not match the user experience.
Build a representative evaluation set
Collect realistic queries
Use anonymized production queries when permitted, supplemented by carefully written examples for important intents that are rare in logs. Cover the distribution of tasks your system receives rather than assembling only easy demonstrations. Include short and long queries, ambiguous wording, names and identifiers, multilingual or mixed-language requests if those occur, and queries that depend on precise terminology.
Group queries by intent or user journey. A single aggregate score can hide a regression in a high-value class: a model may improve broad topical searches while harming error-code lookups or policy questions. Keep the query text exactly as it would enter production, including spelling and punctuation, unless preprocessing is itself the subject of the test.
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Freeze a document snapshot
Record the corpus identifier and snapshot date. Keep document text, chunk boundaries, metadata filters and access rules identical for all candidates. If documents change during the test, a score difference can reflect corpus drift instead of embedding quality.
Record how each document is represented: full document, passage, title-plus-body, or another field combination. A model comparison that changes chunking or fields at the same time is not an embedding-only comparison.
Create relevance judgments
For every test query, identify which documents satisfy the user’s information need. Elastic’s ranking-evaluation guidance requires requests and ratings per query. A binary label (relevant/not relevant) supports recall, MRR and binary precision. Graded labels, such as 0 for irrelevant, 1 for marginally useful and 2 for highly useful, enable DCG and nDCG.
Write a judging rubric before labeling. Define what counts as an answer, whether a duplicate or outdated document is acceptable, and how to handle partially correct passages. If several people judge, give them the same rubric and resolve disagreements consistently. Keep the labels independent of the model being tested where possible; judging only the results produced by one candidate can bias the benchmark.
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Count queries by intent, language, business importance and expected difficulty. A tiny or handpicked set can produce unstable rankings between models. There is no universal required number of queries in the cited guidance; choose a size that covers your task distribution and supports the confidence needed for the decision. Document the sampling rule so another team can reproduce it.
Document the retrieval conditions
Write down every setting that can affect ranking. The comparison record should include:
- model name, version or deployment identifier and encoding date;
- query and document input conventions, including templates or prefixes used for search-specific roles;
- text normalization, language processing, truncation and chunking rules;
- vector dimension, datatype and normalization;
- similarity function, such as cosine similarity, and any score transformation;
- index type, shard or partition layout, search parameters and candidate count;
- metadata filters, access-control filters and exact-match or lexical stages;
- the number of results retrieved before and after reranking; and
- hardware, software versions and timing method for operational measurements.
OpenAI’s example uses cosine similarity for query and document embeddings. Elastic’s material frames dense and sparse vectors as implementation choices, and Cohere documents distinct query and document roles for search. These details matter because changing a role prefix, normalization rule or approximate-search parameter can change results even when the underlying model is unchanged.
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Run the comparison step by step
- Define the decision. State whether the system values retrieval completeness, the first relevant result, graded ordering, visible-list precision, or a combination. Set the cutoff values you will report, such as Recall@10 and MRR.
- Lock the benchmark. Save the query file, document snapshot, labels and configuration manifest. Give the benchmark a version so later runs can be compared.
- Encode consistently. Generate query and document vectors for each candidate using the documented input roles. Do not silently change truncation, batching or preprocessing for one model.
- Build equivalent indexes. Apply the same filtering and search policy. If a model requires a different dimension or index parameter, record that exception and treat it as part of operational fit rather than hiding it.
- Retrieve ranked lists. Run every query through the same production-like path and save document identifiers, ranks and scores. Keep enough results to calculate every planned cutoff.
- Join results to labels. For each query, mark the retrieved documents with their judged relevance grade. Handle unjudged documents according to a declared policy; do not silently treat missing labels as proof of irrelevance.
- Calculate metrics. Produce per-query values first, then aggregate across the suite. Keep the query count and the number of judged relevant documents visible beside each aggregate.
- Inspect failures. Read queries where candidates disagree, where the primary metric falls, and where the top result is clearly wrong. Classify causes such as terminology, negation, stale content, chunk boundaries, missing metadata or a search-configuration error.
- Measure operations separately. Record latency under a stated load, indexing time, storage and serving cost, language behavior and deployment constraints. Do not trade away a measured quality gain without making these costs explicit.
Use metrics that match the task
| Metric or axis | What it measures | Best use | Important qualification |
|---|---|---|---|
| Recall@k | Fraction of all judged-relevant documents that appear in the first k results | Whether enough relevant material enters a context or result budget | Order inside the cutoff does not matter |
| MRR | Average reciprocal rank of the first relevant result | Finding one useful result quickly | Later relevant results contribute nothing once the first one is counted |
| DCG | Graded relevance discounted by rank | Valuing highly relevant items near the top | Absolute values depend on the chosen gain and discount conventions |
| nDCG | DCG normalized by the ideal ordering for the same judgments | Comparing graded ranking quality across queries | Requires graded labels and a defined treatment of unjudged items |
| Precision@k | Share of the first k results that meet the relevance threshold | Keeping an exposed result list focused | It can improve by omitting relevant items, so pair it with recall when completeness matters |
| Operational fit | Latency, cost, dimensions, storage, languages and deployment implications | Choosing a model that can run reliably in production | The cited material does not establish current comparative values for these dimensions |
Recall@k
For a query with eight judged-relevant documents, retrieving four of them in the first ten positions gives Recall@10 = 0.5. Elastic presents this as an illustrative definition, not as a model benchmark or expected performance. Recall does not care whether those four appear at ranks one, five and ten or are clustered at the bottom of the cutoff.
MRR
For each query, find the rank of the first relevant result and calculate 1 / rank. Average those values over queries. A query whose first relevant result is rank one contributes 1; rank four contributes 0.25. Queries with no relevant result in the evaluated list contribute zero under the usual convention, which you should state in the report.
DCG and nDCG
DCG combines a relevance gain with a rank discount, so a highly relevant result near the top contributes more than the same result near the bottom. nDCG divides by the ideal DCG for that query, making scores easier to compare when queries have different numbers or grades of relevant documents. State the label scale and gain function; changing them changes the interpretation.
Precision@k
Precision@k is the number of relevant results in the first k divided by k. Define the threshold first when labels are graded. A threshold of “highly relevant only” answers a different question from “relevant or marginally useful.”
Report aggregate results without hiding regressions
Publish the aggregate metric for each candidate together with the number of queries, cutoff and label policy. Also show distributions or per-query results where practical. Elastic recommends evaluating a representative query suite because a change that helps one query can hurt others.
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- metric by intent, language or business tier;
- the largest wins and losses between candidates;
- queries with no judged relevant item retrieved;
- rank positions of the first relevant result; and
- examples where score order conflicts with human judgment.
Do not declare a winner from a tiny numerical difference without checking its stability. The cited sources do not prescribe one universal significance test or query count. For a high-stakes decision, select an appropriate uncertainty or paired-comparison method, explain it, and account for repeated tuning on the same test set. Keep a separate development set for iteration and reserve the final test set for the decision.
Separate embedding quality from search configuration
A fair model test holds retrieval conditions constant, but production selection may require a second experiment that optimizes each model’s index settings. Keep those phases distinct:
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- Controlled model test: same preprocessing, index policy, candidate depth, filters and cutoffs for every model.
- Configuration tuning: tune search parameters for each finalist while measuring the resulting quality and latency.
- System test: evaluate the complete production pipeline, including lexical retrieval, filters, rerankers and answer generation if present.
If one candidate uses a different vector dimension, compression method or index family, report both the controlled result and the practical configuration. Otherwise readers cannot tell whether a gain came from semantic representation or a more favorable search setup.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.Evaluate operational fit after relevance quality
Relevance metrics answer whether users receive better results; they do not answer whether the system is affordable or fast enough. Measure operational properties under a stated workload and hardware configuration:
- query latency, including percentile values rather than only an average;
- embedding generation latency and throughput for indexing and live queries;
- vector storage, index build time and memory requirements;
- serving or API cost under the expected query and document volume;
- supported languages, scripts and domain terminology;
- maximum input length, truncation behavior and document-update workflow; and
- deployment, privacy, availability and access-control constraints.
Do not fill a comparison table with unverified numbers. The available references identify these as practical selection axes but do not provide current cross-model measurements. Measure them in your own environment and attach the conditions to every figure.
Diagnose common evaluation failures
The benchmark contains only easy queries
Symptom: every model scores highly, but users report failures. Fix: sample the full query distribution, add difficult and ambiguous intents, and report results by segment.
Labels describe document similarity, not user usefulness
Symptom: a topically related passage receives a high label even though it cannot answer the request. Fix: rewrite the rubric around the information need and distinguish partial from complete answers.
Chunking changed between candidates
Symptom: a model appears better after its documents were split into more favorable passages. Fix: freeze chunk boundaries for the controlled test, then run chunking experiments separately.
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Approximate search hides a model difference
Symptom: results vary with index settings or repeated runs. Fix: record the index and search parameters, use a reproducible configuration, and compare approximate retrieval with an exact or higher-recall setting when diagnosing.
Aggregate scores conceal a harmful regression
Symptom: the overall metric rises while an important intent falls. Fix: publish per-query or per-segment results and apply business-weighted analysis only with an explicitly documented weighting rule.
Similarity scores are treated as probabilities
Symptom: a fixed score threshold is copied across models or domains. Fix: calibrate thresholds on judged results for the specific model, corpus and task. Similarity is a ranking signal, not a universal measure of answer correctness.
Unjudged results are counted as irrelevant without disclosure
Symptom: recall and precision change when more documents are labeled. Fix: expand judgments for the evaluation pool and state how missing labels are handled.
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If retrieved passages are supplied to a question-answering or RAG system, keep retrieval evaluation separate from answer evaluation. First measure whether the right evidence appears in the ranked set. Then assess whether the downstream system uses that evidence to produce a useful, supported answer. Retrieval metrics cannot prove that an answer is correct, complete or well cited, and the cited materials do not provide a complete end-to-end RAG evaluation protocol.
At minimum, preserve the retrieved document identifiers for each generated answer. This lets you determine whether a bad answer came from missing evidence, poor ranking, an unsuitable context cutoff or generation behavior. Report the retrieval cutoff used by the answer system, because Recall@k at a larger cutoff may not represent the evidence actually passed downstream.
A reproducible report template
A reader should be able to understand and repeat the comparison from one report. Include:
- Goal: the user outcome and primary metric.
- Queries: source, sampling rule, count, intent distribution and languages.
- Corpus: snapshot date, document count, chunking and metadata policy.
- Judgments: label definitions, judging process, coverage and treatment of unjudged items.
- Models: exact identifiers, input roles, dimensions and preprocessing.
- Search: similarity function, index type, filters, candidate depth, reranking and cutoffs.
- Results: Recall@k, MRR, DCG or nDCG, precision where relevant, per-query breakdowns and failure examples.
- Operations: latency method, workload, hardware, cost assumptions, storage and deployment constraints.
- Decision: the trade-off accepted, the conditions under which it holds and the cases requiring follow-up testing.
Version this report alongside the benchmark assets. If the corpus, labels, model version or index configuration changes, treat it as a new evaluation rather than silently overwriting the old result.
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Select the candidate that best satisfies the declared user outcome on representative queries, not the one with the most impressive standalone similarity score or a vendor benchmark from a different test set. A practical decision can look like this:
- Reject candidates that fail a non-negotiable quality or language requirement.
- Among the remaining candidates, compare the primary metric at the actual production cutoff.
- Check important query segments and failure cases for regressions hidden by the aggregate.
- Verify latency, cost, storage, dimensions and deployment constraints in your environment.
- Run a final system-level test with the chosen retrieval and any downstream ranking or generation stages.
There is no universally best embedding model. The best choice is conditional on your query distribution, corpus, relevance definition, search configuration and operational limits, all of which should be recorded with the result.
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