1) Confirm access
- In ChatGPT: check the model picker. If GPT�? exists in your account, it will be listed there.
- In the API: check the official model list in the docs/console. Don’t rely on third-party screenshots.
- In your org: if you use Team/Enterprise, confirm admin settings and rollout permissions.
2) Pick safe defaults
Most regressions come from changing too many things at once. Treat GPT�? as a model swap first, then tune.
- Keep the same prompt structure you already use for GPT�?.4.
- Use an explicit output format (headings, JSON schema, checklist) so you can compare results.
- Keep temperature / randomness stable (if applicable) during initial evaluation.
3) Use a “two-pass�?pattern for reliability
- Pass 1: draft the answer with your required structure.
- Pass 2: self-check against constraints (missing steps, inconsistent numbers, unsupported claims).
Why this works: it reduces “confident but sloppy�?output without making prompts much longer.
4) Keep prompts “model-agnostic�?/h2>
If your prompt only works on one model name, it’s fragile. The core pieces below tend to transfer well.
- Inputs: what you provide (code, specs, links, constraints).
- Outputs: what you want back (format + length + acceptance criteria).
- Verification: what the model must check before finalizing.