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ChatGPT Prompts for Software Engineers
Most engineers already keep ChatGPT in a browser tab — the value is a prompt that makes it a sharp reviewer instead of a confident guesser. These paste-and-go prompts run a pre-review sweep on your diff, build a ranked ladder of likely root causes for a bug, draft a design doc skeleton, and turn a feature into a real test plan. ChatGPT won't ship your code or own your architecture; it's a fast, tireless reviewer whose every suggestion you check against your tests. Not a ChatGPT lock-in either — these work in Claude, Copilot, or Gemini.
3 free prompts you can run right now
Pre-review sweep: your own PR through a security-and-edge-case lens
The PR is 'done'. Run the pre-review sweep so human reviewers spend their attention on design — not on nits and the missed null check.
You are a staff engineer reviewing a pull request. I will paste the diff and its context. Produce: A) REVIEW TABLE — columns: file/line reference, severity (blocker / major / nit), category (correctness, security, performance, readability, tests), the issue in one sentence, and a suggested fix as a concrete code change. B) EDGE-CASE LIST — inputs and states the diff does not handle: empty, null, concurrent access, oversized input, malformed input, permission-denied. C) VERDICT — approve or request-changes, plus the 2 highest-risk lines in the diff and why. Inputs: [PASTE DIFF] · [WHAT THE CHANGE DOES + WHY] · [LANGUAGE/FRAMEWORK + TEAM CONVENTIONS] Rules: Do not invent code that is not in the diff — reference only pasted lines. Any claim about behavior you cannot see (callers, config, upstream state) must be marked "verify in repo". Never echo secrets or keys, and do not include proprietary code beyond what I pasted.
Security-first PR review: a diff read that hunts the bug class, not the typo
You're reviewing a big PR and low on time. Get a structured read that prioritizes correctness and security over style nits.
You are a staff engineer reviewing a pull request — a review aid whose findings I will verify, not merge blindly. Produce: A) RISK SUMMARY — a one-line verdict (safe / needs-changes / blocked) and the single biggest concern. B) FINDINGS TABLE — each issue: file/area, severity (blocker / major / minor / nit), the specific concern, and a suggested fix — sorted severity-first, with security and correctness above style. C) MISSING TESTS — the behaviors and edge cases this diff changes that the tests don't cover. D) QUESTIONS FOR THE AUTHOR — the 2-3 things I should ask before approving, where intent isn't clear from the diff. Inputs: [PASTE THE DIFF OR KEY FILES] · [WHAT THE PR IS SUPPOSED TO DO] · [LANGUAGE / FRAMEWORK] · [ANYTHING RISKY I ALREADY SUSPECT] Rules: Do not claim a line is buggy without saying why; if you're unsure, list it as a question, not a finding. Don't invent APIs or behavior not visible in the diff. Keep proprietary source out of consumer AI tools per your employer's policy. This drafts the review; you verify every finding before you approve. Do not invent facts, numbers, or details you weren't given.
Root-cause interrogation: a hypothesis ladder from a bug report
Prod bug, vague repro, clock ticking. Structure the investigation before you start changing code at random.
You are a senior engineer running a structured root-cause investigation. I will paste the symptoms and every piece of evidence I have. Produce: A) HYPOTHESIS LADDER — 5 ranked hypotheses. For each: the mechanism, which pasted evidence supports or contradicts it, and the cheapest discriminating test (a log line to add, a git bisect range, a feature-flag toggle, a minimal repro). B) EVIDENCE GAPS — what I have not given you that would most change the ranking, in priority order. C) PREVENTION — run 5-whys on the top hypothesis and name the class-level fix (test, lint rule, alert, type change) that prevents the whole category from recurring. Inputs: [SYMPTOM + WHEN FIRST SEEN] · [PASTE LOGS / STACK TRACE / RECENT DIFFS] · [ENVIRONMENT + RECENT DEPLOYS] Rules: Do not invent stack frames, log lines, or code behavior not in my paste — every hypothesis must cite the evidence line it rests on, and pure speculation must be labeled as such. Verify the discriminating test's result before acting on any hypothesis. Strip credentials and user PII from anything you echo back.
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Subscribe free → Read a sample issue7 more Dev & Engineering prompts in the full set
Here's what's in the rest of the pool — every prompt is free to read in full on its own page; the PromptSharp Dev Brief delivers them to your inbox:
Stack-trace triage: from a wall of errors to the two likeliest root causes
Production is throwing and the trace is a mess. Narrow it to the two most probable causes and the fastest way to confirm each.
You are a senior engineer triaging a failure — a reasoning aid whose hypotheses I will test, not trust. Produce: A) READ — a plain-English restateme
Design doc skeleton with the alternatives you'll actually be asked about
New system or big refactor. Draft the design doc with real alternatives and failure modes before the review meeting drafts it for you.
You are a principal engineer drafting a design document. I will describe the problem and constraints. Produce: A) DOC SKELETON — context and problem,
RFC skeleton: pressure-test the design before you write the code
You're about to build something non-trivial. Draft an RFC that names the tradeoffs and the rejected alternatives, so review is real.
You are a principal engineer drafting an RFC skeleton for a technical design I will own and complete. Produce: A) PROBLEM + CONSTRAINTS — the proble
Test-plan generator: risk-ranked cases from a diff or spec
Feature complete, coverage thin. Generate the test plan ranked by what would actually hurt in production.
You are a test engineer designing a risk-based test plan. I will paste the spec or the diff. Produce: A) TEST MATRIX — columns: behavior under test,
Edge-case hunt: the failure inputs your happy-path tests will miss
Your tests pass but you don't trust them. Enumerate the boundary and failure cases that the happy path never touches.
You are a test engineer building an edge-case and failure-mode inventory for a function or feature I'll implement. Produce: A) BEHAVIOR RESTATEMENT
Toil audit: find the hours a week your workflow is leaking
You suspect the week disappears into builds, reviews, and context switches. Audit it and get an automation plan with paybacks.
You are a developer-productivity engineer auditing one engineer's week. I will paste a work log. Produce: A) TOIL TABLE — columns: recurring task, fr
Ticket breakdown: split an epic into shippable, independently-testable slices
You have a vague epic and a sprint to fill. Break it into small tickets that each ship value and can be tested alone.
You are a tech lead breaking a large piece of work into implementable tickets for my review. Produce: A) SLICE PLAN — the epic split into 4-8 ticket
What Pro adds
The prompts above are free to copy and always will be. Pro is for people who want the PromptSharp Dev Brief working for them every day:
| Every vertical's Pro includes | What it means |
|---|---|
| Every prompt, every day | The full daily prompt set, not just the free rotating sample. |
| MCP access | Pull your prompts straight into Claude, Cursor & any MCP client — scoped to your tier (your vertical / your 3 / entire cross-vertical). Profile-merged responses are a later enhancement. |
| Role-personalized prompts | Every prompt tailored to your role — log in, set your profile once, and prompts auto-fill for you on the web. LIVE (verified end-to-end 2026-07-21). |
| Favorites + network view | Save & search your own prompts in one cross-vertical favorites library (one click), PLUS see the most-favorited across the network (early ranking estimated from usage, sharpens over time). LIVE (verified 2026-07-21). |
| Monthly prompt packs | A curated themed pack of prompts each month on top of your daily set — scoped to your tier. Your first pack lands the first business day after month-end, then every month after. |
| Weekly roundup | A weekly roundup of the week's best prompts, curated from copy + favorite signal. Free readers see a teaser; Pro gets the full set. |
| Suggestion channel | A direct line to request the prompts and verticals YOU want — all subscribers, free and paid. |
| Full prompt archive | The vertical's full back-catalog of prompts, free to browse by date — every prompt we've shipped stays online. |
| MCP access — live | Connect the PromptSharp prompt library inside Claude, ChatGPT, or Cursor and pull prompts where you work. Live today. |
MCP access is live today — connect the prompt library in Claude, ChatGPT, or Cursor in two minutes →
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All 10 Dev & Engineering prompts
Every prompt in this pack has its own permanent page — full text, copy-paste ready, free, no account needed.
Code Review & Quality
- Pre-review sweep: your own PR through a security-and-edge-case lens
- Security-first PR review: a diff read that hunts the bug class, not the typo
Debugging & Root-Cause
- Root-cause interrogation: a hypothesis ladder from a bug report
- Stack-trace triage: from a wall of errors to the two likeliest root causes
Architecture & Design Docs
- Design doc skeleton with the alternatives you'll actually be asked about
- RFC skeleton: pressure-test the design before you write the code
Testing
- Test-plan generator: risk-ranked cases from a diff or spec
- Edge-case hunt: the failure inputs your happy-path tests will miss
Dev Productivity
Frequently asked
What's a good ChatGPT prompt for code review?
Paste the diff and tell ChatGPT exactly what the code is supposed to do, then ask for edge cases, security smells, and unclear naming — ranked, with the reasoning for each. Treat it as a reviewer that never gets tired, not an authority: it will occasionally be confidently wrong, so every finding needs your judgment and your test suite to confirm. Never paste proprietary or secret-bearing code into a consumer AI tool.
How do I use ChatGPT to debug faster?
Make it interrogate a hypothesis instead of guessing an answer. Paste the bug report, the relevant code, and what you've already ruled out, and ask ChatGPT for a ranked ladder of likely root causes with the single check that confirms or kills each. It structures the hunt and surfaces angles you missed; you still run the checks and own the fix. Claude and Copilot handle the same prompt if you prefer them.
Is ChatGPT or a coding-specific model better for these prompts?
The prompts are model-agnostic — ChatGPT, Claude, Copilot, and Gemini all work, and many engineers reach for Claude or Copilot on large diffs for the bigger context window. Model choice matters less than giving the model the real context — the code, the error, the intent — and verifying its output against your tests. Don't trust any model's output until your suite is green.
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