ChatGPT prompt for code generation: Error Taxonomy (With Examples)
ChatGPT · Code Generation · With Examples
You are a senior specialist using ChatGPT for code generation, specifically error taxonomy.
Job: codes humans can use.
Audience: api design. Constraint: retry guidance.
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.A strong ChatGPT code generation error taxonomy prompt names the job, audience, and finish line. This page does that for api design. In practice, one customization pass is enough when the brief is this specific. The with examples version uses practical language and targets few-shot samples and before/after pairs.
When to use this prompt
Picture a api design professional opening ChatGPT with one job: error taxonomy. Without "retry guidance", answers drift. This page prevents that drift. Pair it with a real sample from your portfolio for best results.
Why this prompt works
- The with examples angle adds few-shot samples and before/after pairs without extra fluff.
- Centers codes humans can use so success is observable.
- Separates draft from critique so you can steal the structure later.
- Repeats the hard constraint: retry guidance.
- Forces an output contract: headings, examples, and a final version.
How to use it in ChatGPT
When api design review output, they should see few-shot samples and before/after pairs without hunting for it. Save the winning variant for error taxonomy — teams with a short library ship faster. Redact client names; keep structure. Slug error-taxonomy-examples is your canonical URL for this brief. 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.
Have ChatGPT list assumptions about code generation before drafting. Delete any you cannot defend. For error taxonomy in code generation, the failure mode is usually missing constraint. This prompt blocks that by design — especially "retry guidance". Expect two minutes customizing and two minutes editing for With Examples output. OpenAI models still need your facts for error taxonomy; they cannot invent metrics or brand rules. developers teams bookmark slug error-taxonomy-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 (api design, offer, timeline).
- Confirm the constraint is still visible: retry guidance.
- 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 Error Taxonomy, 3 short sections, bullet list of recommendations, closing line that states retry guidance. Facts you supply appear; nothing invented.
Common mistakes
- Skipping the audience (api design) and hoping ChatGPT infers it.
- Deleting "retry guidance" to save space — that is usually the whole point.
- Using the With Examples prompt but editing it into a vague one-liner.
Expert notes
Practitioners choose With Examples when error taxonomy needs few-shot samples and before/after pairs. ChatGPT from OpenAI still needs your facts — especially for retry guidance.
For code generation, compare this With Examples prompt against the Quick Start and Expert angles on the same use case. Keep the structure that scores higher on codes humans can use.
Comparison / variations
| Approach | When | Tweak |
|---|---|---|
| Bare request | You only need a spark | Write a error taxonomy 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
- api design should recognize themselves in the first paragraph.
- ChatGPT improves when you paste a real snippet or reference still.
- The With Examples variant is for few-shot samples and before/after pairs.
- State the job (codes humans can use) in one line before any style talk.
- Code Generation work needs functions, refactors, tests, and architecture sketches.
FAQ
- What is a ChatGPT prompt for error taxonomy?
- It is a structured brief that tells ChatGPT how to produce code generation output for api design. This with examples version stresses few-shot samples and before/after pairs and keeps "retry guidance" 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 "retry guidance" should stay unless your brief truly changed.
- Why use the with examples angle for error taxonomy?
- Different angles solve different failures. With Examples helps when api design 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.