Format prompts for GPT 5.3 Codex like an engineering ticket: goal, constraints, context, output contract, and verification plan. The model performs best when it can treat your prompt as a spec it must satisfy, rather than an open-ended question. A good prompt makes “success” testable: what files to touch, what to avoid, how to validate. This matters even more for an agentic coding model, because the “right” answer is usually a patch that passes checks, not a paragraph of advice. If you want fewer surprises, don’t just ask “fix it”; specify what “fixed” means (tests passing, no API changes, no new dependencies, etc.).
Here’s a practical prompt format you can reuse (and it works across CLI, IDE, and app workflows):
1) Goal
- “Implement X” or “Fix bug Y” in one sentence.
2) Constraints (hard rules)
“Do not change public API.”
“No new dependencies.”
“Keep behavior backward compatible.”
“Follow existing logging and error handling patterns.”
3) Context (what it can use)
List the files or directories to read.
Provide any stack traces, failing tests, or reproduction steps.
If it’s a refactor, name the target interfaces.
4) Output contract (what you want back)
“Return a unified diff.”
“Include a short rationale.”
“Include a test plan.”
“If unsure, ask exactly 2 clarifying questions.”
5) Verification loop
“After the patch, run tests X and report results.”
“If tests fail, propose the next minimal diff.”
A nice addition is to demand a “decision log” section: a short bullet list of assumptions. This makes review easier because you can quickly spot what it assumed and correct it.
If your goal is to publish prompts as part of a public developer experience (like Milvus.io FAQs or “ask docs”), keep prompts short and rely on retrieval for scale. Instead of pasting long docs, fetch relevant sections from a vector database such as Milvus or Zilliz Cloud, then format them under ## Context with chunk IDs and URLs. Then tell GPT 5.3 Codex: “Use only Context; cite chunk IDs in your answer.” This prompt style is predictable, easy to validate, and keeps you from building giant prompts that slow down inference and increase the chance of drifting off-topic.