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What retrieval accuracy means in a RAG system
A retrieval-augmented generation (RAG) application normally has three measurable stages: retrieval finds candidate passages, reranking orders those candidates by usefulness for the question, and generation writes an answer from the selected evidence. A fluent answer can still be wrong when the relevant passage never enters the candidate set, when the right passage is buried below the context limit, or when permissions and metadata remove it.
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Retrieval therefore has a recall problem before it has a precision problem. Your first goal is to make sure the correct document or chunk appears somewhere in the retrieved set. After that, reranking can move the most useful evidence to the top. Microsoft’s guidance describes this division directly: retrieval optimizes recall, while reranking optimizes precision; reranking also adds latency. NVIDIA’s reference architecture likewise keeps retrieval, optional reranking and generation as separate stages so each can be measured independently.
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Do not claim a universal percentage improvement from one technique. Official guidance published by Microsoft on February 4, 2025 does not provide a cross-system accuracy figure that applies to every corpus, model or workload. Your own frozen evaluation set is the authority for a particular application.
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1. Repair the corpus before changing the model
The highest-leverage failures often occur during ingestion. An embedding model cannot retrieve text that was never extracted, was split away from its context, or is hidden behind stale permissions.
Verify ingestion completeness
- Confirm that every intended file, page and revision was discovered and indexed.
- Check extraction from PDFs, scans and office documents. OCR errors can change identifiers, numbers and names that users search for exactly.
- Inspect tables separately. A row whose column headings were discarded may be semantically meaningless when embedded as isolated text.
- Remove duplicated boilerplate such as repeated navigation, legal footers and headers that can dominate similarity results.
- Record a stable source identifier and a link or location that lets the application show the originating document.
- Store document version or freshness timestamps so obsolete passages can be excluded or deprioritized.
Preserve structure and access context
Keep the heading path, page or section number, table title, caption, document type, language, tenant and permission attributes with every chunk. A chunk containing “timeout” is much more useful when its metadata says it belongs to “Database / Connection settings” than when it is detached from the document hierarchy. Permission metadata must be applied at query time as well as during indexing; returning a relevant but unauthorized passage is a correctness and security failure.
Run a small inspection script or dashboard that shows the original text, extracted text, metadata and final chunk for randomly selected documents. Look for missing titles, broken reading order, concatenated columns, truncated pages, duplicated paragraphs and incorrect source links before tuning search parameters.
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2. Choose chunk boundaries that preserve meaning
Chunking divides documents into smaller units that can be embedded and retrieved. There is no universally correct token or character count. Microsoft’s chunking guidance distinguishes token or character limits, structure-aware splitting and overlapping chunks, and recommends choosing based on document shape, query needs and every model in the pipeline.
Start with meaningful structure
Prefer a heading, subsection, list, procedure, table or paragraph group as the initial boundary. Include the heading path in the chunk text or in a consistently formatted prefix so a retrieved passage carries its subject with it. Keep a table with its header and enough surrounding explanation to interpret each value. For long procedures, keep a complete step or a logically connected group together rather than cutting between a condition and its action.
Test size and overlap instead of guessing
Small chunks can match a precise sentence but omit the definition, qualifier or exception that makes it correct. Large chunks preserve context but dilute similarity and consume the generator’s context window. Overlap can protect information at a boundary, yet excessive overlap creates duplicates, increases index and embedding cost, and can make one passage appear many times in the candidate list.
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Create several chunking variants and run the same labeled query set against each. Vary the size and overlap within limits supported by your embedding and generation models, then compare recall, top-k precision, latency and index cost. Keep the variant that improves the query classes that matter rather than the one that wins on a single aggregate score.
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Keep source identity through splitting
Every chunk should retain a document ID, section or page location, version timestamp and permission fields. If a question requires several sections, those identifiers allow a later stage to group or expand neighboring chunks without losing provenance.
3. Make query and chunk preprocessing identical
Use the same cleaning operations for indexed chunks and incoming queries. Azure’s retrieval guidance explicitly requires matching query preprocessing and chunk preprocessing, and using the same embedding model for both. Differences such as lowercasing only one side, removing punctuation from documents but not queries, or applying different language normalization can make geometrically similar text diverge.
Define one shared preprocessing contract
- Specify Unicode normalization, casing, whitespace handling and punctuation treatment.
- Decide how to handle accents, stemming, stop words, code formatting and field prefixes.
- Apply the same language detection or translation policy to both indexed text and queries where appropriate.
- Version the preprocessing code and store that version with the index.
- Re-embed all affected chunks when a change alters the text presented to the embedding model.
Use one embedding model for both sides of a vector comparison unless the model documentation explicitly supports another arrangement. A model migration is an index migration: mixing vectors from incompatible models makes similarity scores incomparable.
4. Establish lexical, vector and hybrid baselines
Benchmark sparse and dense retrieval separately before combining them. A lexical full-text method such as BM25 is strong when the question contains an exact identifier, product name, error code or rare term. Vector search is strong when the user paraphrases the source or asks conceptually rather than repeating its wording.
| Retrieval mode | What it matches well | Typical weakness |
|---|---|---|
| Lexical or BM25 | Exact identifiers, names, codes and rare terms | Can miss a relevant passage expressed with different words |
| Dense vector search | Conceptual similarity, paraphrases and related wording | Can underweight an exact code or rare token |
| Hybrid search | Combines lexical exactness with semantic similarity | Adds fusion logic and another set of parameters to tune |
Fuse ranked lists deliberately
Hybrid search runs both lexical and vector queries, then combines their ranked lists. Reciprocal Rank Fusion (RRF) is a common approach: each result receives a contribution based on its rank in each list, and the contributions are combined. Microsoft documents weighted vector queries and RRF-based fusion in its hybrid-search flow. Tune vector-versus-lexical weights or RRF settings on labeled queries instead of assuming one source should dominate every request.
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Keep the two baselines in your evaluation report. A hybrid score can improve the aggregate while hiding a regression for exact-term questions or for paraphrases. Report results by query class so you know which retrieval mode is supplying the gain.
5. Retrieve broadly, then rerank for the question
After corpus and baseline fixes, increase the candidate pool so relevant evidence has a chance to enter the set. A larger top-k improves recall but sends more text through later stages and can increase latency, token use and duplicate context.
Use a query-aware reranker
A reranker examines the query and each candidate together, often with a cross-encoder or another query-aware model. It can recognize that a passage containing the exact words is not actually answering the question, and it can promote a passage whose meaning fits the intent. Pass only the highest-scoring evidence to the generator after reranking.
Measure the latency added by reranking separately from retrieval latency. If the service has a strict response-time budget, test a smaller candidate pool, a faster reranker or conditional reranking for difficult query classes. Do not lower the candidate pool until Recall@k shows that the relevant evidence remains available.
Control duplicate and neighboring chunks
Overlapping chunks or adjacent sections can crowd out distinct evidence. Deduplicate by document and location where appropriate, or use a neighboring-chunk expansion step after selecting a high-confidence result. Keep the original ranking and expanded context in logs so you can tell whether a failure came from retrieval, reranking or context assembly.
6. Translate queries only when tests justify it
Query translation can help when the user’s wording is incomplete, vague or composed of several tasks, but an altered query can also change the user’s intent. Keep the original query, every translated variant and the final retrieval result for offline comparison.
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Choose the translation for the failure
- Augmentation: add missing context, such as an identified product, version or domain term.
- Decomposition: split a multi-part question into separate searches, then combine evidence for the complete answer.
- Rewriting: turn vague conversational wording into a clearer search formulation.
- HyDE: generate a hypothetical answer and use its embedding to search for passages with similar concepts.
Reject a rewrite when it lowers intent fidelity, drops a constraint or retrieves less relevant evidence than the original. Evaluate translated and un translated queries on the same labeled set; do not enable every translation method for every request.
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Trustworthy metadata filters narrow the candidate space and remove irrelevant evidence. Useful fields include tenant, user permissions, product, date, language, source and document type. A filter can improve both speed and precision when it reflects the question’s actual constraints.
Validate metadata before enforcing it
- Measure how often each field is missing, stale or incorrectly assigned.
- Confirm that permission values are synchronized with the source system.
- Use a narrow filter only when the user or application has supplied that constraint reliably.
- Run labeled tests with and without each filter to detect suppressed answers.
- Log the filter expression and the number of candidates removed.
Bad metadata can suppress the correct chunk entirely. When a filtered query fails, run a controlled diagnostic search with authorization preserved but nonessential filters relaxed, then repair the metadata rather than permanently broadening access.
8. Tune approximate-nearest-neighbor search after the pipeline is sound
Approximate-nearest-neighbor (ANN) indexes trade some search effort for speed. Azure documentation describes Hierarchical Navigable Small World (HNSW) indexes and exhaustive KNN, along with similarity metrics and the parameters efConstruction, efSearch and m.
Understand the main trade-offs
efConstructioncontrols effort used while building the graph. Increasing it can improve index quality but requires more build compute and storage.efSearchcontrols query-time exploration. Increasing it can improve recall at the cost of latency.mcontrols graph connectivity and affects memory, build cost and search behavior.- Exhaustive KNN searches all vectors and provides a useful accuracy reference, but normally requires more query work than an ANN index.
- The similarity metric must match the assumptions of the embedding model and the index configuration.
Compare ANN settings against an exhaustive or otherwise high-recall reference on the same evaluation set. Tune one parameter family at a time and record recall, latency, memory and build duration. Do this late: a perfect ANN setting cannot recover text lost during extraction or a relevant chunk excluded by a bad filter.
9. Build an evaluation set that exposes real failures
Create a frozen, representative set of questions and label the relevant documents or chunks. Include both positive questions that should be answerable and negative or unanswerable questions for which the system should not invent evidence.
Include distinct query classes
- Exact-term searches containing identifiers, names or codes.
- Paraphrases that express the source content with different wording.
- Multi-hop questions requiring evidence from more than one passage.
- Questions whose answer appears deep in a long document.
- Table and list questions where layout and headers matter.
- Multilingual questions and documents, when the application supports them.
- Permission-filtered and tenant-scoped questions.
- Unanswerable questions that should produce a clear limitation rather than a guess.
Track retrieval metrics separately from answer quality
| Metric | What it tells you | Where to use it |
|---|---|---|
| Recall@k | How often relevant evidence appears within the first k retrieved results | Protecting the candidate set before reranking |
| Precision@k | How much of the first k results is relevant | Measuring noise in the retrieved context |
| MRR | How high the first relevant result appears in the ranking | Detecting whether useful evidence is near the top |
| nDCG@k | Ranking quality when results can have different relevance grades | Comparing ordering and graded usefulness |
| Faithfulness | Whether the generated answer is supported by the supplied evidence | Evaluating generation after retrieval |
| Citation support | Whether cited sources actually support the claims made | Checking provenance and answer traceability |
Microsoft defines precision at K, recall at K and MRR for retrieval evaluation and recommends positive and negative tests. AWS guidance recommends running a fixed evaluation set after every material change, tracking Recall@k and nDCG@k, and refreshing the set as real query patterns drift. Keep the original set stable for comparison, then add a separately versioned sample when production traffic reveals a new failure mode.
Compare four axes, not one score
For every experiment, report retrieval recall, top-k precision or context quality, end-to-end latency and cost. A larger candidate pool or reranker can improve accuracy while increasing latency. Hybrid fusion can improve exact-term coverage while adding query complexity. Richer chunks can preserve context while increasing index size and embedding cost. A single average score conceals these trade-offs.
10. Use a disciplined troubleshooting order
When a chatbot retrieves the wrong chunks, change one layer at a time. The following order prevents expensive model tuning from masking a basic ingestion defect.
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- Inspect actual chunks: look for boundary errors, missing titles, duplicated boilerplate, truncated text and incorrect source links.
- Establish baselines: run dense-only and lexical-only retrieval on the frozen query set.
- Add hybrid fusion: tune weights or RRF settings and verify gains by query class.
- Increase candidate top-k: measure whether Recall@k improves before changing the generator.
- Add reranking: select a query-aware model and record its latency and context-quality effect.
- Add filters and query rewriting selectively: enable them only where labeled tests show a gain without suppressing valid evidence.
- Tune ANN parameters and embeddings last: compare HNSW settings and embedding models using the same evaluation set.
- Monitor drift: repeat checks after corpus, embedding, reranker, index or query changes.
11. Instrument the pipeline for ongoing accuracy
Log enough information to reconstruct each answer without exposing unauthorized content: query ID, original query, translated variants, preprocessing version, filters, index version, retrieved IDs and scores, reranker scores, selected context IDs, latency by stage and model versions. Sample the text under the same access controls used by the application.
Watch for drift signals
- Recall or nDCG falls on the frozen set after an index or model change.
- Exact-term queries increasingly return paraphrases while missing codes or names.
- Duplicate chunks occupy more of the top-k list after a chunking change.
- Permission-filtered queries have a higher empty-result rate than unfiltered diagnostics.
- Latency rises after candidate, overlap,
efSearchor reranking changes. - Production questions shift toward languages, document types or intents absent from the evaluation set.
Schedule a review whenever the corpus, preprocessing, embedding model, reranker, ANN index or query-translation logic changes. Refresh the evaluation set with representative production questions, but preserve the prior version so you can distinguish real improvement from a moving benchmark.
A practical reference pipeline
A robust implementation can follow this sequence:
- Ingest documents and verify extraction, OCR, tables, headings, versions and permissions.
- Normalize text and metadata with one versioned preprocessing contract.
- Split by structure, preserving heading paths and source identifiers; test size and overlap variants.
- Index the same chunks for lexical search and dense vectors using the matching embedding model.
- Apply only validated tenant, permission, language, date, source or document-type filters.
- Run lexical and vector retrieval, then fuse the ranked lists with tuned weighting or RRF.
- Retrieve a candidate pool large enough to protect Recall@k.
- Rerank candidates with a query-aware model and assemble compact, provenance-preserving context.
- Generate an answer that cites the selected evidence and can decline unanswerable questions.
- Evaluate retrieval and answer support separately, then monitor the same metrics after each material change.
This order keeps the system explainable. When an answer is wrong, you can identify whether the source was absent, the chunk was malformed, the filter excluded it, the ranker buried it, or the generator failed to use the supplied evidence.
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