Treat production prompts like application code

OpenAI recommends versioning prompts in your repo, using typed variables, reviewing them in PRs and running evals on every change.

Published October 9, 2026

OpenAI’s prompting guide says it plainly: treat prompts as application code.

Checklist

Keep each production prompt in a named, versioned module (e.g. prompts/supportReply.ts).
Replace string-template variables with typed function parameters or validated inputs.
Put role/tone guidance in the system message; task details and examples in user messages.
Review prompt changes in the same pull request as the behavior they change.
Run prompt tests and eval cases every time you publish; catching regressions early is cheaper than in production.

Bonus: put stable content first so repeated prefixes benefit from prompt caching.

Related tips

On xAI's Responses API, append every output item (including encrypted reasoning) to the next request. Dropping it hurts quality and cache hits.

Oct 16, 2026

Project instructions override your global custom instructions inside that project, and project-only memory keeps context from leaking across.

Oct 14, 2026

reasoning_effort controls how hard the model thinks; verbosity controls how long the answer is. Adjust them instead of rewriting prompts.

Oct 13, 2026