If a RAG agent returns the wrong passage, trace the answer back through the retrieval pipeline: check what entered the index, how it was chunked and embedded, which candidates search found, and what ultimately reached the model. The failure may be in any of those stages—not just the vector search. A versioned set of real questions and retrieval traces can show where the answer first goes missing.
Start by finding where the expected passage disappears
For each test question, identify the source passage that should support the answer. Then follow that passage through the pipeline. Log the preprocessed query, retrieved candidate IDs and text, scores, filters, ranking steps, and the final chunks passed to the model. Record the index version or freshness information needed to tell which source snapshot was searched.
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- Check the final context first. If the expected passage is in the retrieved candidates but not among the chunks sent to the model, investigate filtering, reranking, result limits, and context assembly.
- Check the index if the passage never appears. Confirm that the source and expected text were ingested, transformed, chunked, embedded, and persisted. Then inspect query and index preprocessing, embeddings, and the retrieval method.
- Check whether the passage actually supports the answer. If it reaches the model but lacks necessary context, investigate chunk completeness. If it is sufficient but the answer is still unsupported, examine the generation prompt and grounding behavior rather than treating it as a search miss.
- Change one stage at a time. Compare retrieval relevance, answer quality, and latency on the same question set before and after each change.
Microsoft’s Azure Architecture Center guidance recommends evaluating each search step. This approach also helps distinguish a retrieval defect from a generation defect instead of relying on a plausible-sounding answer as proof that search worked.
1. Chunk boundaries remove the context the answer needs
A chunk can be individually searchable yet still be a poor unit of evidence. A boundary may separate a heading from the paragraph it explains, split a definition from its qualifications, or combine unrelated sections into one vector. In the last case, the embedding represents several ideas at once, which can make a relevant passage harder to retrieve precisely.
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What to inspect
- Read the stored chunk, not just the original document or a search-result snippet. Check whether it contains the complete claim, its conditions, and the nearby context needed to interpret it.
- Look for headings stored separately from their content, lists divided across chunks, tables stripped of their row or column labels, and unrelated topics merged together.
- Compare the chunk text and boundaries with the expected source passage for several real questions. One example is not enough to establish a good setting for a varied corpus.
What to change
Choose a chunking method that preserves the structure and meaning of your documents. Fixed-size chunks are straightforward to implement; structure-aware, semantic, or hybrid approaches can better preserve coherent passages when documents have useful headings or varied subtopics. Tune the approach against your own question set rather than treating a general recommendation as a universal optimum.
Microsoft Learn gives 512 tokens with 25% overlap—128 tokens—as an initial chunk-size and overlap starting point in its guidance. That is vendor guidance, not a proven best setting for every corpus. The same consulted page reports an 8,191-token maximum input length for Azure OpenAI text-embedding-3-small and estimates roughly 6,000 words for common OpenAI models. That model-specific input limit is not a recommended chunk size; check the current limit for the model and service you use.
2. The user’s wording does not match the source’s terminology
A person may ask a conversational question while the source uses a product name, formal term, abbreviation, or exact identifier. Vector search can match related meaning without matching the wording; keyword search can match exact terms but miss a paraphrase. A query that performs poorly in one mode may work better with a different retrieval strategy.
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- Compare the original question with the source language and the preprocessed query. Check whether key terms, abbreviations, names, or identifiers have been removed or changed.
- Test representative questions that use both the source’s terminology and ordinary user phrasing. Check whether relevant passages appear in the candidate pool, not just whether the final answer sounds right.
What to change
Compare keyword, vector, and hybrid retrieval on the same versioned question set. Hybrid search combines keyword and vector retrieval, which can help when questions need both exact terminology and semantic matching. A semantic ranking step can then reorder candidates by meaning. Azure AI Search documentation describes hybrid retrieval and semantic ranking; behavior and configuration are service-specific.
Query translation can also help bridge wording differences. Options include rewriting a question, adding related terminology, decomposing a compound question, or generating a hypothetical answer passage (HyDE) to improve retrieval. Treat these as alternatives to evaluate, not transformations that are automatically beneficial: compare retrieval results and downstream answer quality against the unmodified query.
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3. Preprocessing or embeddings make query and index representations incompatible
Similarity search depends on how both the indexed text and the incoming query are represented. If cleaning, tokenization, normalization, or embedding choices differ in ways that change meaning, a relevant passage may no longer rank well. A seemingly harmless text-cleaning rule can also remove words that reverse a claim.
What to inspect
- Compare the text used to build the indexed embedding with the query text after preprocessing. Look for inconsistent transformations, lost punctuation in identifiers, altered casing where it matters, or dropped terms.
- Check for negation and other meaning-bearing words. Microsoft’s embedding guidance specifically warns that “not” can carry important meaning.
- Confirm that the query and indexed passages use compatible embedding logic. If an embedding model or preprocessing pipeline changed, verify that the index was rebuilt or otherwise brought into compatibility.
What to change
Make query and document preprocessing deliberate and compatible. Preserve negation, conditions, product names, codes, and other terms that distinguish one passage from another. Evaluate any normalization or rewriting change with questions that depend on those distinctions, including examples where adding or removing “not” would change the answer.
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4. Candidate limits, filters, or ranking hide a relevant passage
Retrieval and context assembly are separate decisions. A relevant chunk might be found but excluded by a metadata filter, fall below a result limit, lose a reranking comparison, or be omitted when the final context is assembled. Conversely, a larger candidate pool may improve the chance of finding relevant evidence while adding latency and more noise for later stages to handle.
What to inspect
- Check whether the expected passage appears in the initial candidates, the reranked list, and the final model context. Record the filter and ranking decisions at each stage.
- Confirm that filters match the intended document, tenant, permissions, and metadata values. Check which text fields are searchable and which fields are actually included in the context.
- Compare the candidate set with the final context. Inspect result limits and ordering, including whether truncation or context assembly displaced a useful passage.
What to change
Measure candidate recall before tuning the final context or reranker: a reranker cannot recover a passage that never entered its candidate pool. Then measure whether reranking improves query-to-chunk relevance enough to justify its added processing latency. Test candidate-pool size and final-context size separately so that a recall improvement is not confused with the effects of extra noise, latency, or token use.
Apply access controls during retrieval. A broad candidate search that exposes documents a user is not authorized to see is not a quality improvement; it is a security failure.
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5. The index is stale, incomplete, or enriched incorrectly
If the expected source passage does not exist in the searchable index, ranking changes will not fix the problem. A RAG ingestion pipeline typically has distinct stages for chunking, enrichment, embedding, and persistence; inspect the output of each stage rather than assuming that a successful source upload means every expected passage is searchable.
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- Verify that the expected document and passage are present in the indexed representation and that the index reflects the source version you intend to search.
- Check document identity and metadata, including whether updates, deletions, or duplicate records left an outdated version or confusing competing copies.
- Inspect enrichment and transformation outputs for missing text, malformed structure, incorrect metadata, or a passage assigned to the wrong document.
What to change
Trace a known source document through ingestion, chunking, enrichment, embedding, and persistence. Keep enough document identity and freshness information to connect a retrieved chunk to its source and determine when it was indexed. Microsoft Foundry documentation emphasizes that content preparation, retrieval configuration, and prompt design all affect RAG quality; grounding cannot compensate for a passage that was never indexed correctly.
Choose retrieval changes by measuring their trade-offs
These approaches address different failure patterns. Compare them on the same representative queries and inspect both retrieval results and the answers that follow; there is no evidence here for one universally best configuration.
| Choice | Useful distinction | Trade-off to measure |
|---|---|---|
| Keyword vs. vector vs. hybrid retrieval | Exact terminology and identifiers vs. semantic matching; hybrid combines keyword and vector retrieval. | Which mode retrieves the expected passages on your question set. |
| Fixed-size vs. structure-aware or semantic chunking | Implementation simplicity vs. preserving document structure and coherent passages. | Passage completeness and retrieval relevance across your corpus. |
| Larger candidate pool vs. smaller final context | More opportunities to include relevant evidence vs. a tighter context with less noise. | Candidate recall, latency, token use, and answer quality. |
| Reranking vs. no reranking | Query-aware ordering of candidates vs. avoiding an additional ranking step. | Relevance or precision improvement against added processing latency. |
Microsoft’s Azure Architecture Center and Azure AI Search guidance describe these retrieval choices and their evaluation considerations. Their service-specific examples are useful starting points, not a substitute for measuring your own workload.
Keep retrieved content secure and treat it as untrusted
Retrieved passages are input to a model, not trusted instructions. A document can contain malicious directions intended to influence generation. Process and retrieve content with prompt-injection risks in mind, enforce authorization at retrieval time, and instruct the model to use retrieved text as evidence rather than as authority to change its behavior. Grounding can reduce unsupported responses, but it does not guarantee that an answer is correct.
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