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Error Analysis for LLM Applications
Error analysis examines a representative sample of model or application failures, labels what went wrong, and measures how often each failure occurs.
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Résumé
It helps teams prioritize fixes by frequency and severity instead of relying only on anecdotes, but the findings are only as good as the sample, labels, and outcome criteria.
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When an LLM application fails, the visible answer may be only one part of the cause. A wrong response can stem from an unclear prompt, poor retrieval, stale data, a tool-call error, model behavior, or a mismatch between the product goal and the evaluation. Error analysis makes failures concrete by reviewing traces and assigning categories. Start with a defined outcome and collect a sample of successes and failures from representative workflows. Label errors with a consistent taxonomy—such as factual error, missing constraint, refusal, retrieval miss, invalid tool call, formatting failure, or unsafe action. Record severity, task type, model and prompt version, and whether a human had to intervene. Use examples and annotation guidance so reviewers apply labels consistently. Counts can show common failures, while severity and user impact reveal which issues deserve attention first. Stratify the sample where needed so rare but costly cases are not drowned out by frequent low-impact issues. A random production sample can reveal broad patterns; targeted samples can investigate a particular failure, but their rates should not be presented as population prevalence. OpenAI’s evaluation guidance recommends examining traces and using structured graders to find failure modes; its evaluation flywheel example discusses reading failing traces and applying labels. After a fix, rerun the same cases and a holdout set to check for regressions. Error analysis guides work, but it does not prove causality unless the proposed fix is tested.
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The Future of Error Analysis for LLM Applications
Evaluation platforms may automate trace collection, failure clustering, and regression tracking, but human review will remain important for ambiguous outcomes. Larger systems need failure taxonomies that span retrieval, models, tools, and user experience. Future teams should connect incidents to versioned eval cases and measure whether fixes reduce impact, not just the raw failure count. Data privacy and representative sampling will remain central. Better dashboards may help connect failure categories to severity, user impact, and model changes over time routinely and meaningfully.
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A team labels 50 failing traces by retrieval miss, unsupported answer, formatting error, or tool failure.
A random sample measures common failures while a separate targeted sample investigates a rare safety issue.
An analyst records model version and severity to see whether a change helps one task but harms another.
A prompt fix passes old failure cases and is checked against a held-out evaluation set.
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What is Error Analysis for LLM Applications?
Error analysis examines a representative sample of model or application failures, labels what went wrong, and measures how often each failure occurs. It helps teams prioritize fixes by frequency and severity instead of relying only on anecdotes, but the findings are only as good as the sample, labels, and outcome criteria.
Why review traces instead of only reading user complaints?
The observable workflow can reveal causes beyond the final response.
How should rare but high-severity errors be handled?
Targeted sampling helps find rare issues, but does not estimate their population rate.
What does measuring error frequency alone miss?
A rare severe issue can matter more than a frequent minor one.
After applying a fix, which check can reveal regressions?
A fix should be tested against known failures and unseen cases.
What limitation applies to a targeted failure sample?
Targeted samples are selected for discovery rather than unbiased rate estimation.
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