Evaluate and Observe OpenAI Workflows
Measure OpenAI outcomes, safety, tool trajectories, latency, cost, and drift with versioned cases, calibrated graders, trace evidence, production feedback, and release gates.
By the end
You will be able to
- Define multidimensional success and must-not-regress thresholds before changing a workflow.
- Build representative, edge, adversarial, incident, and holdout evaluation cases.
- Combine deterministic, human, and calibrated model grading appropriately.
- Connect trace and production evidence to release, rollback, and regression decisions.
Define the evaluation contract
Name the workflow, target users, observable outcome, required evidence, forbidden behavior, quality rubric, safety and permission gates, latency and cost budgets, important slices, and release decision before running a candidate.
Use multiple dimensions. A higher average quality score can conceal a critical permission, injection, accessibility, language, or tool-side-effect regression. Declare which criteria block release regardless of the aggregate.
Build versioned cases from real work
Cover common tasks, important user segments, boundary conditions, ambiguous inputs, missing context, prompt injection, unavailable tools, tool failures, refusals, and prior incidents. Preserve a holdout set to detect overfitting.
Version inputs, reference evidence, expected output properties, allowed and forbidden trajectories, data approvals, and case provenance. Synthetic cases can increase coverage, but reviewers must confirm they represent the product rather than an easy artificial distribution.
Choose and calibrate graders
Use deterministic checks for schemas, exact fields, calculations, citations, permissions, postconditions, latency, and cost. Use blinded human review for consequential or subjective judgment. Use model graders for scalable rubric signals when they are calibrated against labeled examples.
A model grader is not ground truth. Version its model and prompt, test positive and negative examples, measure agreement and bias across slices, and route uncertain or high-impact disagreement to people. Do not let the candidate grade itself as the only evidence.
Grade traces and compare candidates
For tool workflows, evaluate the trajectory as well as the final answer: tool choice, arguments, call order, retries, approvals, intermediate evidence, side effects, and stopping behavior. Trace grading helps locate where a workflow failed instead of assigning every miss to the final model response.
Run baseline and candidate with the same cases, prompts, tools, budgets, environment, and graders. Report per-case and per-slice results, critical failures, distributions, uncertainty, grader disagreement, latency, usage, and cost. Honor predeclared ship, hold, and rollback rules.
Close the production feedback loop
Observe privacy-reviewed request and trace identifiers, application, prompt, model and tool versions, latency, usage, status, validation, tool outcomes, policy events, user feedback, and terminal state. Use allowlists, redaction, access control, sampling, and retention limits.
Convert reproducible production failures and incidents into evaluation cases with complete provenance. Track drift by version and slice, investigate threshold breaches, and preserve a tested rollback. Dashboards reveal signals; owners still decide containment, repair, and release.
Practice activity
Build an OpenAI release evaluation
- Define at least six quality, safety, permission, trajectory, latency, and cost criteria with numeric ship, hold, and rollback thresholds before testing.
- Create twelve versioned cases covering representative work, edge cases, adversarial inputs, tool failures, a prior incident, important user slices, and a protected holdout.
- Assign deterministic, blinded human, or calibrated model graders; validate the model grader against labeled examples and measure disagreement.
- Run baseline and candidate under identical conditions, grade final outputs and traces, report slice-level evidence, and make a signed release decision with rollback triggers.
What to produce
- A versioned twelve-case suite, multidimensional rubric, grader contracts and calibration, thresholds, case provenance, and data-use approval.
- A reproducible baseline-versus-candidate report with trace evidence, per-slice metrics, critical failures, disagreement, latency, cost, and a release or rollback decision.
Reflect before continuing
Which aggregate result looked acceptable until a critical slice or tool trajectory was examined?
Evidence
Sources and verification
- Evaluation best practicesOpenAI · verified 2026-07-25
- Working with evalsOpenAI · verified 2026-07-25
- GradersOpenAI · verified 2026-07-25
- Trace gradingOpenAI · verified 2026-07-25
Knowledge check
Make it stick.
Choose the strongest answer for each question. Your attempts become part of your device-local transcript.