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A fast RAG pipeline can still retrieve the wrong evidence. Low latency tells you how quickly retrieval runs; it does not tell you whether the returned chunks answer a question. To evaluate embeddings, test retrieval against representative queries and your own corpus, using human-judged relevance labels and several ranking metrics. Measure latency and cost alongside quality, not as substitutes for it.
What embedding evaluation should measure
An embedding model represents text as vectors, and a retrieval system uses those representations and a scoring method to find candidate passages. A high similarity score is a ranking signal—not proof that a passage contains the answer. The practical test is whether the system retrieves useful evidence for the questions your application actually receives. Microsoft’s guidance recommends evaluating embedding performance through retrieval on real-world queries and content: Generate Embeddings Phase.
Keep two outcomes distinct: retrieval quality (whether useful evidence appears in the results) and operational performance (how quickly and expensively it gets there). A configuration can be fast but miss relevant material, or retrieve well at a cost or latency your application cannot accept. Track both.
Build a representative evaluation set
Evaluation is only as useful as its test cases and relevance judgments. Assemble queries that reflect your corpus and users, then mark which documents or chunks are relevant to each query. Microsoft’s retrieval guidance describes using test queries with known relevant-document information and including positive and negative examples: Information-Retrieval Phase.
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Include more than easy paraphrases
- Questions phrased in different ways, including domain vocabulary and terminology users may not share with the source material.
- Exact identifiers, names, codes, or other queries where literal matching may matter.
- Ambiguous requests and queries the corpus cannot answer, where those cases occur in your application.
- Positive cases with known relevant evidence and negative cases where no suitable evidence is present.
Label relevance at the level your retriever returns—documents or chunks—and record examples that have not been judged. An unlabelled relevant result can otherwise be mistaken for an irrelevant one, or an evaluation can appear more complete than it is. Microsoft Foundry’s RAG evaluator reports missing ground-truth labels as “Holes”: RAG Evaluators.
Keep the test set useful over time
Treat queries and labels as a maintained evaluation asset. Preserve a consistent set for controlled comparisons, add representative cases when real failures reveal a gap, and distinguish new or unjudged cases from judged ones. Public benchmarks can help with broad comparisons, but they may not represent your corpus or query distribution; validate candidates on your own workload.
Choose metrics that expose different retrieval failures
No single score describes retrieval quality completely. Use a small suite of metrics, and choose k—the number of returned results included in a measurement—to reflect how many chunks your downstream pipeline can actually use. Microsoft’s retrieval guidance discusses retrieval measures including recall, precision, and MRR; Databricks explains DCG and NDCG for its retrieval-quality evaluation feature: Information-Retrieval Phase and Evaluate AI Search retrieval quality.
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|---|---|---|
| Recall@k | Share of the known relevant documents or chunks that appear among the first k results. | When missing evidence could make an answer incomplete. |
| Precision@k | Share of the first k results judged relevant. | When irrelevant context adds noise or undermines trust. |
| MRR (mean reciprocal rank) | How highly the first relevant result ranks across queries. | When the earliest relevant result is especially important. |
| DCG or NDCG | Ranking quality with position effects and, where used, graded relevance. DCG reflects accumulated utility; NDCG normalizes against an ideal ordering. | When relevance has degrees and placement in the ranking matters. |
Databricks recommends DCG@10 as the primary metric for its own retrieval-quality evaluation feature, but that is product-specific guidance, not a universal choice. Its documentation also cautions that a single metric cannot tell the whole story. Pick measures that fit the consequences of failure in your application.
Run controlled comparisons before tuning
Start with a baseline and change variables in a way that lets you tell what caused a result. For every run, record the query set and judgments, embedding model and dimensions, chunking approach, retrieval mode, candidate depth, final top-k, and any reranker. Compare the same test cases and calculate the same metrics each time.
Compare retrieval approaches
Vector search, full-text search, and hybrid search can behave differently across query types and corpora. Hybrid retrieval combines keyword and vector search; it may help when exact terms matter alongside semantic similarity. Microsoft’s Azure AI Search overview describes these retrieval approaches: RAG and Generative AI in Azure AI Search. Test each against the same queries and labels rather than assuming one mode wins across the board.
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Sweep settings as a set of experiments
- Vary top-k and candidate depth to see how many useful chunks are surfaced and how much irrelevant context arrives.
- Test chunk size and boundaries; a relevant passage split awkwardly or mixed with unrelated material may be hard to retrieve or use.
- Compare embedding models and, where applicable, dimensions using the same workload.
- Test reranking if initial retrieval finds useful candidates but orders them poorly. Reranking can improve ordering, but adds processing.
- Measure latency and cost for each configuration as well as retrieval metrics. More candidates can give a reranker more material, but can also increase processing time and expense; more final context may reduce misses while adding tokens and noise.
Microsoft Foundry’s evaluator documentation describes evaluating configuration choices and surfacing results for RAG systems: RAG Evaluators. Avoid changing chunking, model, search mode, and top-k all at once: even an improved score then will not tell you which change helped.
Diagnose errors instead of blaming the embedding model
Inspect failed queries by type, not only as one aggregate score. Averages can hide a system that works well on paraphrases but fails on exact identifiers, or that retrieves evidence for common topics while missing less frequent ones.
- Relevant evidence never appears: Check whether the corpus contains it, whether indexing included it, and whether chunk boundaries leave the information retrievable.
- Exact names or codes are missed: Compare vector retrieval with full-text or hybrid search; literal matching may matter for these queries.
- Related but unhelpful chunks dominate: Review terminology mismatch, chunk size, candidate depth, and relevance labels. A similarity score alone cannot establish answerability.
- Useful chunks appear too low: Check ranking metrics and consider whether reranking improves placement enough to justify its added processing.
- Results look poor only for certain query types: Add or refine representative evaluation cases and examine whether the issue is query coverage, content preparation, retrieval settings, or the model.
Fine-tuning is not the automatic next step. Microsoft’s embedding guidance advises evaluating prompt engineering or constrained decoding before fine-tuning and notes that poor training data can degrade retrieval. If you do fine-tune, compare it on held-out queries and labels so that improvements are not limited to examples used during development: Generate Embeddings Phase.
Evaluate generated answers separately
Strong retrieval scores do not guarantee strong generated answers. Once retrieval is measured, evaluate the response as a separate stage: does it stay grounded in retrieved material, cover the question, remain relevant, and state facts correctly? Microsoft’s end-to-end evaluation guidance distinguishes answer evaluation from retrieval: Large Language Model End-to-End Evaluation Phase. Ragas documents answer and RAG metrics that can help assess dimensions such as faithfulness and relevance: List of available metrics and Evaluate and Improve a RAG App.
If the retrieved evidence is relevant but the answer is incomplete or incorrect, investigate generation, context assembly, and instructions rather than treating the retrieval score as an answer-quality verdict. If the evidence itself is poor, continue diagnosing retrieval and corpus preparation.
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