Describe failures before scoring them
LLM eval error analysis starts with concrete failed outputs and asks how they failed before asking how often. A useful taxonomy is grounded in observed behavior and leads directly to cases or controls. This page owns one search job: cluster failures before adding evaluation metrics. It does not promise a universal product ranking, an undisclosed benchmark, or hands-on results that are not present in the evidence ledger.
Give developers a source-led, reproducible answer for how to cluster failures before adding evaluation metrics, with explicit version and stop conditions. In practice, that means separating documented behavior from inference, naming the consequence of being wrong, and defining the evidence that would change the decision.
For the llm eval error analysis decision, the surrounding LLM Evaluation Design: From Failure to Test Case guide defines the nearest architectural boundary and prevents this page from absorbing a broader search job.
Preserve the evidence behind each label
Maintain an error ledger with the original input and permitted context, observed output, expected behavior, provisional category, consequence, suspected mechanism, reviewer confidence, and proposed follow-up. The artifact should be portable enough for another engineer to inspect without relying on a private chat transcript or the memory of the person who ran it.
For llm eval error analysis, the source ledger uses current first-party material from OpenAI and OpenAI to define documented concepts and interfaces. Those sources do not prove performance on this site's hypothetical setup, so every comparative or operational conclusion remains tied to the recorded artifact and a local verification step.
After the llm eval error analysis evidence is recorded, use Build an LLM Evaluation Dataset; it covers the adjacent implementation handoff without duplicating the protocol here.
Build the taxonomy from cases
The order matters for llm eval error analysis. Starting with tooling or a score before the evidence boundary is defined makes later results hard to interpret. Keep each step small enough that its input, authority, output, and failure state can be reviewed independently.
- Draw a time-bounded sample that contains failures and apparently successful outputs.
- Review cases without a fixed metric menu and assign provisional descriptions in plain language.
- Merge overlapping descriptions into a taxonomy while retaining examples and reviewer disagreement.
- Prioritize by consequence and frequency, then convert each actionable category into an evaluator or control.
Record the exact llm eval error analysis configuration and environment beside the artifact, but do not invent a version number in evergreen copy. At execution time, pin the tested release, preserve command output or trace evidence, and stop when the next action requires new authority or an unverifiable assumption.
Error-analysis funnel
Resist metric-first categorization
For llm eval error analysis, the architecture flags three recurring risks for this family: metrics are chosen before error analysis, an LLM judge is not calibrated, and the evaluation dataset leaks or drifts. They are not abstract caveats; each can make a polished result unusable for the decision this page owns.
- Starting with a preferred metric can force different failures into the same convenient category.
- Reviewing only escalations misses common low-grade friction that dominates user experience.
- Treating the first taxonomy as permanent hides new failure modes after prompts, models, or tools change.
Treat a llm eval error analysis failure label as the start of investigation, not as an explanation. Preserve the case, identify which evidence or control was missing, and rerun one changed condition at a time. That discipline separates a tool limitation from a bad task definition, weak context, an unsafe permission, or a broken test harness.
Test whether categories drive action
Verification for “cluster failures before adding evaluation metrics” needs a stopping rule that another engineer can apply. The checks below favor direct artifacts and observable state over confidence, verbosity, or vendor reputation. A failed check keeps the conclusion provisional even when the generated output appears convincing.
| Check | Evidence to retain | Stop condition |
|---|---|---|
| Grounding | Every category has representative source cases | A label exists without an example |
| Separation | Categories describe distinct corrective actions | One case fits every category |
| Actionability | The category maps to a test, prompt, tool, or policy change | The label only says low quality |
Run the llm eval error analysis gate against both an expected success and at least one denied, malformed, or recovery path. Store disagreements and residual risk beside the result. If the evidence cannot distinguish a system failure from an evaluation failure, improve the instrument before using its score to approve a release.
When the llm eval error analysis gate exposes a neighboring problem, continue with LLM Evals: Build Evidence Before Metrics and carry this page's evidence record into that step.
Failure-category quality gate
Turn stable failures into checks
Create a metric only after a failure category has stable examples and a decision attached to it. Keep uncertain categories in the ledger until reviewers can distinguish them reliably. This rule applies to the documented search job, not to every use of llm eval error analysis. A different repository, data boundary, model, tool set, or consequence requires a new dated check.
End the llm eval error analysis record with the owner, next review trigger, and one of four outcomes: proceed within the tested boundary, reduce scope, gather missing evidence, or reject the approach. This preserves a useful negative result and prevents scheduled editorial copy from implying an experiment that was never run.