Evals implementation guide: from traces to release decisions
A practical reading path for instrumenting an AI application, building datasets, validating scorers, and deciding whether a change is ready to ship.
An eval workflow needs evidence of what the application did, a definition of acceptable behavior, and a decision that follows from the result. These guides cover each step, from the first useful trace to a release gate and production feedback.
Start with one user task. Write down what success looks like and which failures would make the result unacceptable. Then follow the sequence below, or jump to the problem you need to solve.
The implementation sequence
| Step | Guide | What you should have afterward |
|---|---|---|
| 1. Capture execution | OpenTelemetry for LLM applications | A trace connecting the request, retrieved context, model calls, and tool results |
| 2. Preserve test cases | Production traces to eval datasets | Versioned inputs, expected behavior, and dependencies that can be replayed |
| 3. Validate the evaluator | Calibrate an LLM judge | Reviewed examples, measured disagreement, and a held-out validation set |
| 4. Score the right behavior | Evaluate agent tool calls or RAG evaluation metrics | Checks that distinguish failure in an intermediate step from failure in the final answer |
| 5. Define the release decision | Set eval release thresholds | Explicit pass, fail, and inconclusive rules tied to a named baseline |
| 6. Deploy a specific version | Prompt versioning and rollback | A release record, request-level version identifiers, and a rollback procedure |
| 7. Learn from production | Find failure patterns in AI conversations | Reviewed incidents that become new cases or operational fixes |
For deployment requirements, read Self-hosted LLM evaluation before choosing where traces, datasets, and scorers will run. That decision affects the setup across the entire sequence.
Start where your system is weakest
You have logs but cannot explain a bad answer. Start with tracing. Check whether the record contains the final context the model saw, the tool result, and the configuration version. A timestamp and a final response are rarely enough to reconstruct a multi-step failure.
You have examples but no repeatable evaluation. Start with dataset construction. Decide whether the case reruns the application or only rescores an existing output. Preserve the state and dependencies needed for that test boundary.
You have scores but reviewers do not trust them. Start with judge calibration. Inspect false passes and false failures separately. If the expected behavior is ambiguous, improve the rubric before increasing the volume of automated scoring.
You have a green dashboard but releases still regress. Start with release thresholds and per-case comparisons. Check for missing results, critical failures hidden by averages, and CI jobs that report scores without enforcing a decision.
Keep the first workflow narrow
Choose a task with an observable outcome, such as answering from a specified document or updating a record in a test environment. Preserve a small set of varied cases, including a confirmed failure. Use direct assertions wherever the correct result is available as structured data.
Run an accepted baseline and a candidate on the same cases. Inspect changed outcomes before deciding which scores deserve to block a release. A small curated suite provides regression coverage for those cases; it does not establish a population-wide success rate.
Once the process is useful, expand coverage by task type and use production review to discover missing cases. The offline versus online evals guide explains how those two sources of evidence complement each other.
Choose tooling after defining the workflow
Use the CI/CD tool comparison when you know what the gate must enforce. For a broader product decision, compare Braintrust and Langfuse, Braintrust and LangSmith, or Braintrust and Arize.
Evaluate a candidate tool with the same dataset and release policy you intend to use. Check whether someone can trace a failed check back to the exact input, output, scorer, and application version. That is a more useful acceptance test than a feature checklist alone.