OpenAI API and Agent Application Development
Lesson 4: Testing, Evaluation, and Production Operations
Lesson Objectives
By the end of this lesson, students should be able to:
- Build unit, integration, regression, and adversarial tests
- Create evaluation datasets and measurable rubrics
- Monitor quality, latency, cost, safety, and failures
- Version prompts, tools, models, and datasets for rollback
Lesson Content
AI application testing must include more than happy paths. Unit-test deterministic code and tool wrappers. Integration-test model-to-tool behavior. Build regression cases from real failures. Add adversarial prompts, prompt injection, malformed files, unsupported requests, sensitive-data attempts, and tool outages.
Evaluations need a representative dataset and rubric. Measure task success, factual support, schema validity, unsafe-action rate, refusal correctness, latency, token or cost use, and human correction time. Compare changes against the same dataset before release.
Production monitoring should capture request IDs, model and prompt versions, tool errors, safety events, cost, and user feedback without exposing secrets. Rollouts should be staged, reversible, and protected by feature flags or versioned configuration. Model changes can alter behavior even when application code does not change.
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