Engineering5.0 · 198 ratings

Bug Repro — Minimal Failing Case

Turn a vague bug report into a clean repro: steps, expected vs actual, environment.

Role-BasedOutput-FormatConstraints

Prompt

**Role:** Senior support engineer who has filed 1000+ bug reports to engineering teams. You know that the difference between a fixed bug and a closed-as-can't-repro bug is the quality of the first 60 seconds of reading.

**Context:** A user reported: "[the vague bug report]". They're using [product version] on [OS/browser]. Their description includes some hand-waving and some specifics.

**Task:** Convert the report into a minimal reproducible test case that an engineer can run in under 90 seconds and either reproduce or definitively rule out.

1. Title: imperative + specific. Not "Login broken" but "Login fails with HTTP 500 when email contains '+' character on iOS Safari 17."
2. Environment: product version, OS, browser, locale, account type, anything else relevant.
3. Steps to reproduce: numbered, exact, copy-pasteable URLs and inputs.
4. Expected behavior: what the user expected to see, in one sentence.
5. Actual behavior: what they saw — quote any error message verbatim.
6. Minimal repro: the smallest possible variation. Strip away every confounding variable.
7. Workaround (if any): what the user is doing now to get unblocked.

**Constraints:**
- Quote error messages exactly — never paraphrase
- Include console errors, network 4xx/5xx, and any user-visible state
- If you can't repro on your machine, say so — list what you tried
- No speculation about cause unless you have evidence

**Output format:** GitHub issue template · 7 sections · screenshots/HAR file references in [brackets].

How to use this prompt

  1. 1

    Copy the prompt above and paste it into ChatGPT, Claude, or Gemini — or open it in the visual Studio to edit each part on a canvas and run it with your own key.

  2. 2

    Replace any bracketed placeholders with your specifics. The more concrete your context and constraints, the sharper the result — see the 5-part prompt structure.

  3. 3

    Run it, then refine. Ask the model to critique and improve its own answer with self-critique prompting.

Techniques in this prompt

Role-Based

Assigns the model an expert persona so it adopts the right vocabulary, depth, and standards for the task.

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Output-Format

Specifies the exact shape of the result — sections, a table, JSON, a word count — so the output is predictable and ready to use.

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Constraints

Sets the rules and boundaries — tone, length, what to avoid — that keep the output on-target.

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Build on this prompt

Open it in the visual Studio to wire it into a full workflow with your own API key — or learn the craft behind prompts like this.

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