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When an Ask-Your-Docs chatbot gives a wrong answer, inspect the evidence it received before changing the model. The fault may be a missing or stale source, broken parsing, a poor chunk boundary, a restrictive filter, weak ranking, or generation that ignores good evidence. Work through these seven checks in order, changing one variable at a time.
1. Does the correct answer exist in the source?
Search the original documents or knowledge store for the specific fact the chatbot should have used. Verify that the material is current, accurate, and not contradicted elsewhere. If the source is absent or wrong, retrieval cannot supply the right answer. Salesforce’s troubleshooting guidance also recommends checking for obsolete, contradictory, or incorrect content in retrieved chunks and improving the knowledge store: Salesforce Help: Troubleshooting Knowledge Retrieval for Agents.
2. Was the source ingested and parsed correctly?
Compare the indexed text with the original document. Check whether PDF text, tables, headings, and paragraphs were extracted intact and in the right reading order. A table may lose the association between a label and its value; a heading may become detached from the section it describes. Those are ingestion or parsing problems, not failures that a new prompt can reliably fix.
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3. Do chunk boundaries preserve the needed context?
Inspect the actual indexed chunks rather than relying on the source document’s appearance. A relevant sentence may be separated from the heading, definition, exception, or date that makes it interpretable. Conversely, a very large chunk can include enough unrelated material to obscure the useful passage.
Compare chunk size and, where appropriate, section-aware splitting while keeping other settings fixed. GOV.UK notes that split method and chunk size can affect performance and context relevance. Salesforce’s troubleshooting guide recommends looking for chunks that are too small or too large and information split across chunks. There is no universal best chunk size established by these sources; choose using your corpus and repeatable tests.
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4. Are query wording, filters, or permissions excluding the evidence?
Check what the system actually searched, not only what the user typed. An ambiguous query may fail to match a specific passage, while an over-specific query may omit useful wording. Review metadata filters, dates, categories, and other constraints to see whether they eliminate relevant documents. Also verify that the user has permission to access the evidence: a retrieval system should not surface content the user is not authorized to see.
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5. Which candidates were retrieved, and how were they ranked?
For a failing query, record the retrieved chunks, their ranks, and the source passage for each. This reveals whether useful evidence was never found or was found but pushed below irrelevant candidates. If vector and hybrid search are both available, compare them against the same corpus and query set rather than assuming either strategy is always superior.
Salesforce recommends comparing retrieval approaches and inspecting chunk ranking. Keep track of the exact configuration used for each run; changing the search method, filters, and chunking together makes it difficult to identify what affected the result.
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6. Does retrieval work across a fixed test set?
A single query can reveal a defect, but it cannot show whether a change improves retrieval overall. Build a small, stable set of representative queries that includes answerable questions, unanswerable questions, and the known evidence that should support each answerable query. Run the same set against each configuration.
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Use retrieval metrics to examine different failure modes:
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- Precision@K: the fraction of the top K retrieved results that are relevant.
- Recall@K: how much of the relevant evidence appears among the top K results.
- Mean reciprocal rank (MRR): how highly the first relevant result appears across queries; earlier ranks contribute more.
Microsoft’s RAG information retrieval guidance recommends testing positive and negative examples and evaluating all test queries. These metrics describe retrieval behavior, not whether the final answer is faithful or complete. The cited guidance does not establish a universal acceptance threshold, ideal top-K value, or best chunk size.
7. Does the answer follow the retrieved evidence?
If relevant, correctly parsed evidence reaches the model, inspect the answer-generation stage. Check whether the answer contradicts or overlooks the retrieved context, whether instructions require evidence-based responses, and whether the system has a clear way to say that the context is insufficient. Unsupported questions should not pressure the chatbot to invent an answer.
Measure answer quality separately from retrieval quality. Salesforce defines measures such as Context Precision, Faithfulness, and Answer Relevance in its Knowledge/RAG quality metrics documentation. AWS’s RAG performance metrics guidance covers context relevance and coverage, correctness, completeness, faithfulness, and citation precision and coverage. A citation should point to evidence that supports the associated claim; its presence alone does not establish that the claim is correct.
How to compare a fix without muddying the diagnosis
Hold the corpus and query set constant, then change one retrieval or generation variable at a time. Compare the outcomes that match the suspected failure:
- Relevant-result precision and evidence recall or coverage.
- Rank of the first useful passage.
- Answer faithfulness and completeness.
- Citation correctness and coverage.
- Operational constraints such as token budget and latency.
There is no broadly applicable optimal chunk size, top-K value, reranker, or pass threshold established by the cited guidance. Choose settings based on your own corpus, evaluation set, and operational needs.
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