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Reproducible Bug Summary Prompt Generator

⚡ Quick Answer

A reproducible bug summary converts a customer's messy problem report into the structured format engineering actually works from: expected versus actual behavior, numbered reproduction steps, environment, and severity. The generator below builds that summary from the customer's own words and marks every missing detail [CHECK:] instead of inventing it.

⚡ Reproducible Bug Summary Prompt Generator

Configure your facts below to generate a production-ready, constraint-locked prompt.

100% Client-Side

What this generator does

Search for help with bug reports and you will find two worlds that do not talk to each other. Developer and QA pages offer excellent bug-report templates — for people who already know the version number, the environment, and how to write reproduction steps. Support pages, meanwhile, barely cover bug reports at all: the customer-facing side of the workflow, where the raw material is an annoyed paragraph typed one-handed on a phone, has no tooling. The handoff between those two worlds — support agent reads the paragraph, engineering needs a reproducible summary — is where bugs go to slow down. Engineering replies "can't reproduce, need more info"; support goes back to the customer days later; the customer has already churned or forgotten the details.

This generator owns that handoff. You paste the customer's report exactly as it arrived (redacted), add whatever your team already knows — version, environment, frequency, what you have tried — and the generated prompt produces the summary engineering expects: a precise title, a customer-impact sentence, expected versus actual behaviour, numbered reproduction steps built only from what was actually reported, the environment, the workaround if one exists, and a suggested severity with its reasoning. Anything the report does not establish — the app version, the exact step where it breaks, whether it happens on Wi-Fi as well as mobile data — is not guessed. It is marked [CHECK:], and the summary ends with the shortest possible list of questions to ask the customer, so one reply from them makes the report reproducible.

What a good bug summary contains

  1. A title that carries the fault. Area — what fails — condition: "iOS app — session not persisted — user returned to login on every open since the latest update." An engineer scanning a board should understand the bug before clicking.
  2. A customer-impact sentence. Who is affected and what they cannot do. Severity arguments are won and lost on this line, and support — not engineering — is the side that knows it.
  3. Expected versus actual, stated separately. "Expected: the app stays signed in between opens. Actual: the login screen appears on every open." Mixed into one paragraph, the two blur; separated, the defect is unmistakable.
  4. Reproduction steps built from evidence only. Numbered, minimal, and honest about gaps. A step the customer never described is worse than a [CHECK:] marker — engineering will follow it, fail to reproduce, and discount the whole report.
  5. Environment and frequency. Product version, OS, browser or device, and how often it happens: every time, intermittently, or once. Intermittent bugs need the conditions (network, account state, time of day) captured or explicitly marked as unknown.
  6. What support already tried. So engineering does not re-ask the customer to restart, reinstall, or clear the cache when your team has already ruled those out — and so the customer is not asked twice.

Who it's for

How it works

  1. Redact the customer's report. Names, emails, account IDs → tokens; access tokens, cookies, and log secrets → . Bug reports get forwarded, quoted, and screenshotted — write them as if they will travel, because they will.
  2. Fill what you know, leave the rest empty. Version, environment, frequency, workaround, what you tried. Empty fields are instructions to the model to mark [CHECK:] — that is the design working, not a gap in your input.
  3. Copy the generated prompt and run it in Claude Sonnet 5.5, GPT-6.1 / GPT-4o, or Gemini 4 Argon.
  4. Send the [CHECK:] questions to the customer — they are written to be pasted into your reply as-is — and update the summary with their answers before or after filing.
  5. File the summary, labelled severity as a suggestion. Engineering triages priority against everything else in the queue; your severity line, with its reasoning, is evidence for that decision — not a verdict they must accept.

The prompt template

PROMPT TEMPLATE 
You are a support engineer preparing a bug report for the engineering team. Convert the customer's problem report into a reproducible summary using ONLY what the customer and the support agent have actually observed.

CONTEXT — use only what is supplied here:
- Customer's report, in their words (redacted): [paste]
- Product and version: [e.g. Web app v4.2 / iOS app 3.9.1 / not stated]
- Environment: [browser + version, OS, device / not stated]
- Customer account type: [Free / Pro / Enterprise]
- How often it happens: [every time / intermittent / happened once / not stated]
- Workaround, if any: [describe / none known]
- What support has already tried: [list / nothing yet]
- Log excerpts or error text (redacted — remove tokens, cookies, emails, order IDs): [paste / none provided]

INSTRUCTIONS:
1. Write a one-line title in the form: [Area] — [what fails] — [condition, if known].
2. Write a one-sentence customer-impact statement: who is affected and what they cannot do.
3. State "Expected behaviour" and "Actual behaviour" as separate single statements, based strictly on the report.
4. Build "Steps to reproduce" ONLY from actions the customer or agent actually described. Number them. Where a step, setting, version, or error code is missing or unclear, write [CHECK: what is missing] in its place — never fill gaps with plausible steps.
5. State frequency exactly as reported. If timing or triggering conditions are unknown, mark them [CHECK:].
6. End with "Questions for the customer": the shortest list of questions whose answers would make this report reproducible.
7. Suggest a severity (Blocker / Major / Minor / Cosmetic) with one sentence of reasoning based on impact and frequency. Label it a suggestion — final triage belongs to engineering.

NEGATIVE CONSTRAINTS:
- Do not invent steps, versions, error codes, menu names, or causes. A guessed step wastes more engineering time than a marked gap.
- Do not state a root cause. Suspected causes go only under a clearly labelled "Unverified hypotheses" line, or are omitted.
- Do not promise the customer a fix, a fix date, or a workaround that has not been confirmed.
- Do not include customer names, emails, account IDs, tokens, or any personal data. Describe the shape of the data ("a 14-digit order reference"), not the value.

OUTPUT FORMAT: Title → Customer impact → Expected behaviour → Actual behaviour → Steps to reproduce (numbered) → Environment → Frequency → Workaround → Already tried → Questions for the customer → Suggested severity + reasoning.
Best model for this: Claude Sonnet 5.5 — it separates reported fact from inference reliably on long, messy customer threads. Gemini 4 Argon when the ticket history is very long and must be summarised in the same pass; GPT-6.1 if your tracker ingests the output as structured fields.

Worked example

The customer's report (as received): "The app keeps logging me out!! Every time I open it I have to sign in again. Started after the update last week. I'm on iPhone and I use Face ID and it just goes straight back to the login screen. Really annoying when I'm trying to check my deliveries on the go."

Filled prompt (excerpt): account type "Pro"; environment "iPhone — iOS version not stated"; frequency "every time (per customer)"; already tried "nothing yet"; version "not stated".

Output excerpt:

Title: iOS app — session not persisted — user returned to the login screen on every app open since last week's update
Customer impact: A Pro customer cannot stay signed in, so every delivery check requires a full Face ID sign-in.
Expected behaviour: After signing in once, the app opens to the home screen on subsequent launches.
Actual behaviour: The login screen is shown on every app open, immediately after Face ID authentication completes (per customer).
Steps to reproduce:
1. Open the iOS app (version [CHECK: app version — ask the customer to read it from Settings or the App Store page]).
2. Sign in with Face ID.
3. Close the app fully (swipe away). [CHECK: does it also happen when the app is only backgrounded, not closed?]
4. Reopen the app — the login screen appears again.
Questions for the customer: Which iPhone model and iOS version are you on? Does it happen on both Wi-Fi and mobile data? Did you reinstall the app after the update?
Suggested severity: Major — a core flow (staying signed in) is broken every time for this customer, with no workaround; upgrade if other reports share the pattern.

Count the [CHECK:] markers: two, each converting a guess the agent might have made ("it's the force-close behaviour", "it's version 3.9") into a question that takes the customer ten seconds to answer. That is the difference between a summary engineering can act on and one they bounce.

Guardrails

Frequently Asked Questions

Practical answers covering model choice, workflow integration, and compliance rules.

Six things: a title that states the fault and its condition; a customer-impact sentence; expected behaviour and actual behaviour stated separately; numbered reproduction steps built only from what was observed; the environment (product version, OS, browser or device) with frequency; and a missing-evidence section listing what is still unknown. This template adds two support-specific items most developer templates omit: what support has already tried, and the workaround if one exists — the two things engineering asks for first and tickets usually lack.

File the summary anyway, and say plainly what was tried and what was observed — that is information, not failure. A report that states "support could not reproduce on [environment]; customer reproduces every time on [their environment]" points engineering at the environmental difference, which is often where the bug lives. What you must not do is paper over the gap: an unreproduced report dressed up with confident, invented steps sends engineering chasing a failure mode that may not exist. Mark the unknowns [CHECK:] and let the questions to the customer close them.

By construction, not by tone. Facts are what the customer or agent directly observed: the screen shown, the action taken, the frequency reported. Everything else — causes, triggers, "it's probably the update" — is inference, and the template forces inference into one of two places: a clearly labelled "Unverified hypotheses" line, or out of the report entirely. If you cannot point to the sentence in the customer's message or the test your team ran that supports a claim, it is a guess, and it gets labelled or cut.

Anything that identifies a person or unlocks an account: customer names, email addresses, account IDs, order numbers, access tokens, session cookies, API keys, and payment details. Replace them with tokens ([CUSTOMER], [ORDER_ID]) or <REDACTED>, and describe the shape of the data where the shape matters ("a 14-digit order reference", "a JWT session token"). Stack traces and log excerpts need the same pass — they routinely contain tokens and email addresses in headers that nobody notices until the report has been forwarded three times.

It is the smallest set of steps, inputs, and conditions that reliably triggers the bug — nothing extra. It matters because reproduction is the gate to everything downstream: an engineer who can trigger a failure can instrument it, fix it, and prove the fix; one who cannot is guessing at code paths. Customers almost never send a minimum example — they send a story. This generator's job is to extract the minimal steps from the story, and to mark precisely which step or condition is still missing before the example is complete.

Internal links

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