Turn an AI demo into a dependable tool
A repeatable delivery workflow based on OpenAI documentation, evaluation methods and GitHub learning resources.
0101 / Specify inputs and acceptance criteria
Write down the task, the audience, the facts to preserve and the inferences to avoid. Make success checkable: required fields, valid formatting and no invented promises. Use these criteria to judge prompts instead of one impressive answer.
0202 / Complete a minimal server-side request
Follow the official quickstart, then add input and output bounds, timeouts and quota handling. Keep credentials on the server. Explain failed-request accounting and preserve a manual path so an AI outage does not disable the whole site.
0303 / Improve from failure cases
Collect normal, incomplete, oversized, contradictory and edge-case inputs with expected behavior. Classify failures before changing the prompt. Run the same cases after model or rule changes to find improvements and regressions.
0404 / Version and roll back
Version prompts, configuration and tests together. Check success, exhausted quotas, upstream errors and duplicate submissions before release. Expand the test set with observed failures and document boundaries in release notes.
Use portable evaluation methods rather than depending on the legacy Evals platform. API usage generally incurs costs. Review outputs, especially public promises, prices and factual claims.
Sources & further reading
An original Qiyue synthesis of these sources. Suggested actions are not platform guarantees.