ChatGPT prompt for code generation: Type Narrowing (With Examples)
ChatGPT · Code Generation · With Examples
You are a pragmatic coach using ChatGPT for code generation, specifically type narrowing.
Job: make invalid hard.
Audience: ts/python. Constraint: examples.
Working style: practical. Optimize for few-shot samples and before/after pairs.
Process:
1. Restate the brief in one sentence and list missing facts as questions.
2. Draft the artifact.
3. Stress-test it against the constraint.
4. Return a final version plus 3 numbered improvements.
Output contract: use headings, short paragraphs, and concrete examples. No filler. No invented statistics.
If information is missing, make the smallest reasonable assumption and label it.Looking for a ChatGPT prompt for type narrowing? Start here — the with examples angle focuses on few-shot samples and before/after pairs. Teams report saving 15–30 minutes per task when prompts include constraints up front. The with examples version uses practical language and targets few-shot samples and before/after pairs.
When to use this prompt
When ts/python tackle type narrowing inside code generation, the hard part is not the model — it is the brief. Functions, refactors, tests, and architecture sketches. This prompt encodes that context so ChatGPT does not guess. Bookmark this page if you run type narrowing weekly.
Why this prompt works
- The with examples angle adds few-shot samples and before/after pairs without extra fluff.
- Keeps code generation vocabulary specific instead of buzzwords.
- Centers make invalid hard so success is observable.
- Uses practical language that matches how practitioners actually brief work.
- Asks for a self-check pass, which cuts hallucinated details.
How to use it in ChatGPT
Add one real example from your last week of work so the model mirrors your domain. For code generation work, paste chatgpt is a conversational language model used for drafting, research, coding, and structured thinking as a one-line reminder above the prompt. Use practical follow-ups only — mixing tones mid-thread confuses OpenAI models. This With Examples path is for ts/python who care about make invalid hard. If the first answer is vague, reply with the constraint again: examples.
Start with the ugly first pass for type narrowing. Name what is wrong in five bullets, then rerun in ChatGPT. For type narrowing in code generation, the failure mode is usually no example. This prompt blocks that by design — especially "examples". Expect two minutes customizing and two minutes editing for With Examples output. OpenAI models still need your facts for type narrowing; they cannot invent metrics or brand rules. developers teams bookmark slug type-narrowing-examples when few-shot samples and before/after pairs matters.
Step-by-step
- Open ChatGPT and paste the prompt block unchanged.
- Replace placeholders with your project facts (ts/python, offer, timeline).
- Confirm the constraint is still visible: examples.
- Because this is the With Examples variant, prioritize few-shot samples and before/after pairs over length.
- Reply with one fix list, then regenerate the final version only.
Example output
Example output shape: H1 with Type Narrowing, 3 short sections, bullet list of recommendations, closing line that states examples. Facts you supply appear; nothing invented.
Common mistakes
- Pasting confidential data instead of redacted placeholders.
- Using the With Examples prompt but editing it into a vague one-liner.
- Deleting "examples" to save space — that is usually the whole point.
Expert notes
This ChatGPT page targets "make invalid hard" for ts/python. ChatGPT is a conversational language model used for drafting, research, coding, and structured thinking. The practical tone keeps few-shot samples and before/after pairs visible while you edit.
Workflow tip: save three winning outputs for type narrowing and diff them. The shared skeleton becomes your team standard inside ChatGPT.
Comparison / variations
| Approach | When | Tweak |
|---|---|---|
| Bare request | You only need a spark | Write a type narrowing piece. |
| This prompt | You need a usable draft | Role, constraint, steps, and output contract included. |
| Plus examples | Voice must match yours | Paste 120 words of your writing or a still you like, then rerun in ChatGPT. |
Key takeaways
- Ship a draft, then tighten. Do not chase a perfect first token.
- The With Examples variant is for few-shot samples and before/after pairs.
- State the job (make invalid hard) in one line before any style talk.
- Keep examples visible in the prompt, not only in your head.
- ts/python should recognize themselves in the first paragraph.
FAQ
- What is a ChatGPT prompt for type narrowing?
- It is a structured brief that tells ChatGPT how to produce code generation output for ts/python. This with examples version stresses few-shot samples and before/after pairs and keeps "examples" in the instructions.
- How do I customize this ChatGPT code generation prompt?
- Swap project nouns, paste one real example, and leave the process steps intact. The constraint "examples" should stay unless your brief truly changed.
- Why use the with examples angle for type narrowing?
- Different angles solve different failures. With Examples helps when ts/python need few-shot samples and before/after pairs. It is not longer for its own sake — each line has a job.
- Is this ChatGPT prompt updated for 2026?
- Yes. Pages follow our SEO Bible, refresh on a rolling schedule, and include current best practices for OpenAI models. Always verify facts before client delivery.