Turn retrieval errors into specific indexing and ranking fixes

Define the observable retrieval chain

LangChain’s retrieval documentation describes modular retrieval components. Build a diagnostic record that follows your actual chain: source availability, eligibility, parsing, chunking, candidate generation, reranking, and final context. Not every system exposes every stage, so record observable limits rather than inventing internal explanations. Start from a question with a clear expected evidence requirement. A wrong final answer alone does not prove that retrieval failed; the necessary context may have been present and ignored.

Evidence: Retrieval [1]

Work a first-broken-stage example

In an authored warranty case, the answer omits a regional exclusion. First confirm that the approved source contains the exclusion. If parsing dropped it, fix extraction. If a chunk contains it but a wrong date filter removes it, fix eligibility. If it is eligible but absent from candidates, investigate retrieval. If candidates contain it but the final context does not, inspect reranking or assembly. The same visible answer failure can therefore require several very different engineering interventions.

Record evidence rather than a vague label

For each failed case, retain question, corpus revision, required source spans, stage outputs, final context, answer, and judgment reason. Add a narrow diagnosis with supporting observations, such as “qualifier present in candidate C but removed by truncation.” Keep unresolved cases explicitly unresolved when instrumentation is insufficient. Avoid confidently labeling every semantic mismatch as an embedding defect. A useful diagnosis should identify a testable next change and explain which observation would confirm or refute the hypothesis.

Compare intervention-specific experiments

Test one targeted repair against the same failure and nearby controls. A parsing fix should restore the missing qualifier without corrupting other layouts. A candidate-depth change should be evaluated with its cost and context effects. A reranker change should be tested on a frozen pool as well as end to end. Preserve regressions and unsuccessful attempts; they constrain the next hypothesis. Do not quietly relabel failed cases or remove them from the suite after an intervention makes the average look better.

Turn analysis into a durable regression case

After a repair, retain the minimal evidence needed to reproduce the original failure under the agreed data policy. Record the root cause, changed component, observed retest, and remaining limits. Keep the critical expected behavior independent of one exact answer phrasing. A failure taxonomy becomes valuable when it points to owners and repeatable tests, not when it simply increases the number of dashboard categories. Use recurring causes to prioritize corpus and instrumentation improvements before commissioning more generic evaluation metrics. Retain a control where retrieval is correct but generation ignores the exception, preventing a retrieval repair from receiving credit for a different stage’s improvement.

Common questions