ChatGPT Prompts for Developers: Debug, Refactor & Review

ChatGPT prompts for developers work best when you stop asking for "help with my code" and start pasting the actual code, the actual error, and the exact constraints you're working under. The model can't read your repo, guess your stack, or know that you can't add a dependency — so a good developer prompt hands it everything it needs and asks it to show its reasoning before it commits to an answer.

This is a copy-paste pack: 35 prompts grouped by the work you actually do — debugging, code review, refactoring, writing tests, documentation, architecture, and learning a new stack. Each one is built to be filled in with your real code and pasted straight into ChatGPT, Claude, or any capable model. Swap the bracketed parts, keep the structure.

How to make these prompts actually work

Three habits separate a useful coding answer from a confident-sounding wrong one.

Paste the real artifact. The code, the full stack trace, the failing test output, the schema. Not a paraphrase. Models hallucinate the parts you leave out, and a summarized bug is a bug the model never actually sees.

State your constraints up front. Language version, framework, what you can't change, performance budget, style guide. "Fix this" with no constraints gets you a rewrite that drops your error handling. See the RCTCO structure for a clean way to stack role, context, task, constraints, and output format.

Ask for reasoning and trade-offs, not just an answer. The single biggest upgrade to any developer prompt is "explain why before you give the fix" and "list the trade-offs of each option." That forces the model to reason step by step — chain-of-thought prompting — which catches mistakes a one-shot answer hides.

Weak: My function is slow, how do I fix it?

Strong: Here is a Python function [paste code]. It processes ~50k rows and takes ~8s; I need it under 1s. I'm on Python 3.11, can use the standard library and pandas but no new dependencies. Profile it conceptually, name the likely bottleneck, then give me a faster version and explain the trade-off in readability.

The strong version names the language, the data scale, the target, the allowed tools, and asks for reasoning. That's the template for everything below.

Debugging prompts

When you're stuck on a bug, the model needs the symptom, the code, and what you've already ruled out.

I'm getting this error: [paste full stack trace]. Here's the relevant code: [paste code]. Here's what I expected to happen vs. what actually happened: [describe]. Walk through the execution path step by step, identify the root cause, and explain why it produces this exact error before suggesting a fix.

This code works on my machine but fails in CI: [paste code]. CI environment: [Node 20, Ubuntu, etc.]. Here's the CI error: [paste]. List the most likely environment-specific causes ranked by probability, and tell me what to check first for each.

This bug is intermittent — it fails roughly 1 in 10 runs: [paste code]. Identify every source of non-determinism (race conditions, ordering, timing, shared state), and for each one explain how it could cause the failure I'm seeing: [describe symptom].

I've narrowed a bug to this function but can't see it: [paste function]. I've already confirmed the inputs are [describe verified inputs]. Don't suggest changes yet — first trace the function line by line with those inputs and tell me the value of each variable at each step.

Here's a failing test and the code under test: [paste both]. The assertion that fails is [paste]. Explain whether the bug is in the test's expectation or in the implementation, and justify your answer with the actual logic.

Why these work: they give the model the trace plus the code plus your hypotheses, and they explicitly delay the fix until after the diagnosis — so you get a root cause, not a guess that papers over the symptom.

Code review prompts

Use the model as a fast first-pass reviewer before a human ever looks. Tell it what to prioritize.

Review this pull request diff as a senior engineer: [paste diff]. Focus on correctness, edge cases, and security in that order. For each issue, give the line, the severity (blocker / should-fix / nit), why it matters, and a concrete suggested change. Skip style nits unless they cause real bugs.

Here's a function I'm about to merge: [paste code]. List every edge case and failure mode it doesn't currently handle — empty inputs, nulls, concurrency, overflow, malformed data — and rank them by how likely they are to bite in production.

Audit this code for security issues only: [paste code]. Check for injection, unsafe deserialization, secrets in code, missing input validation, and authorization gaps. For each finding, explain the exploit scenario, then the fix.

Review this code for readability and maintainability, assuming a junior dev will own it in six months: [paste code]. Point out anything that's clever-but-unclear, name the confusing parts, and suggest clearer alternatives without changing behavior.

Here are two implementations of the same feature: [paste A] and [paste B]. Compare them on correctness, performance, and maintainability. Recommend one, and be explicit about what you'd trade away by choosing it.

Why these work: a review prompt is only as good as its priorities. Naming the lens (security, edge cases, maintainability) and demanding a severity plus a fix per finding turns vague feedback into an actionable checklist.

Refactoring prompts

Refactoring is where models love to quietly change behavior. Pin them down.

Refactor this function for readability without changing its behavior: [paste code]. Keep the same inputs, outputs, and side effects. After the refactor, list exactly what changed and confirm each change is behavior-preserving.

This module has grown to [N] lines and does too much: [paste code]. Propose a decomposition into smaller units with single responsibilities. Show the new structure first as a list of functions/classes with one-line descriptions, then wait for my OK before writing code.

Reduce the duplication in this code: [paste code]. Extract shared logic, but don't over-abstract — if two things only look similar by coincidence, leave them separate and tell me why.

Convert this imperative loop-heavy code to a more declarative style: [paste code]. Language is [X]. Show the rewrite, then tell me honestly whether the declarative version is actually clearer here or whether the original was fine.

I want to make this code more testable: [paste code]. Identify the hard-to-test parts (hidden dependencies, global state, side effects), then refactor to inject those dependencies. Explain how each change makes a specific test easier to write.

Why these work: every prompt locks behavior in place and asks the model to report its diffs, so a "cleanup" can't silently drop a branch. Asking it to push back on over-abstraction keeps the refactor honest.

Writing test prompts

Good test prompts force coverage of the cases you'd skip when you're tired.

Write unit tests for this function using [pytest / Jest / JUnit]: [paste code]. Cover the happy path, boundary values, empty and null inputs, and at least two error cases. Before writing, list the cases you'll test and why each one matters.

Here's a function and its current tests: [paste both]. Find the gaps — what behavior is untested? List the missing cases ranked by risk, then write tests for the top three.

Generate table-driven tests for this function in [Go / Rust / etc.]: [paste code]. Each case should have a name, inputs, and expected output. Include the tricky cases: off-by-one boundaries, unicode, and the largest realistic input.

Write tests that would fail if someone reintroduced this bug: [describe the bug you just fixed and paste the fix]. The goal is a regression test that pins the correct behavior. Explain what the test asserts and why it catches the regression.

I'm practicing test-driven development. Here's a spec: [describe the feature]. Write the failing tests first, one at a time, and stop after each so I can implement before you write the next.

Why these work: they make the model enumerate cases before coding, which surfaces the boundaries and error paths you'd otherwise forget — and the TDD prompt keeps it in lockstep with you instead of dumping a whole suite at once.

Documentation prompts

Docs are where models genuinely save hours — if you give them the code and the audience.

Write a docstring for this function: [paste code]. Style: [Google / NumPy / JSDoc]. Document parameters, return value, raised exceptions, and one usage example. Don't restate the obvious — explain the non-obvious behavior and any gotchas.

Generate a README section for this module: [paste code or describe the API]. Audience is a developer integrating it for the first time. Include a one-paragraph overview, a minimal working example, and a "common mistakes" list.

Explain what this legacy code does, in plain English, for someone who's never seen it: [paste code]. Go function by function. Flag anything that looks like a bug or a deliberate-but-surprising choice, and mark which is which.

Write clear inline comments for this code: [paste code]. Comment the why, not the what — skip lines that are self-explanatory and explain the decisions, edge cases, and non-obvious constraints.

Draft a CHANGELOG entry and a migration note for this change: [paste diff]. Audience is users of the library. Be specific about what breaks, what they need to change, and a before/after example.

Why these work: they pin the audience and the doc style, and they tell the model to skip the obvious and explain the why — which is the difference between docs that help and comments that just narrate the code.

Architecture & design prompts

For design questions, give the model your constraints and force it to lay out trade-offs instead of declaring a winner.

I need to design [feature/system]. Constraints: [scale, latency, team size, existing stack, budget]. Propose 2-3 architectural approaches. For each, give a diagram-in-words, the main trade-offs, and where it breaks down at scale. Don't pick one yet — I want to compare.

I'm choosing between [option A] and [option B] for [problem]: [describe both and your context]. Build a decision matrix across the dimensions that matter for my case (operational cost, complexity, failure modes, team familiarity), then give your recommendation with the reasoning.

Here's my current data model: [paste schema]. I need to support [new requirement]. Propose schema changes, explain the migration path, and call out any change that would require downtime or a backfill.

Review this API design for a public REST endpoint: [paste spec or routes]. Check for consistency, versioning, error-handling conventions, and pagination. Flag anything that would be painful to change after launch.

I want to add caching to this flow: [describe the flow]. Where should the cache live, what's the invalidation strategy, and what staleness can I tolerate? Walk through the failure modes — stale reads, thundering herd, cache stampede — and how to handle each.

Why these work: architecture has no single right answer, so the prompts demand options and explicit trade-offs rather than a verdict. That keeps you in the decision seat with the model doing the legwork.

Learning a new stack prompts

When you're ramping on an unfamiliar language or framework, use the model as a patient senior who explains why.

I know [language X] well and I'm learning [language Y]. Teach me [specific concept, e.g. Rust ownership] by translating this idiom I already know: [paste X code]. Show the Y equivalent, then explain what's genuinely different in the mental model, not just the syntax.

I'm new to [framework]. Here's what I'm trying to build: [describe]. Show me the idiomatic way to do it in this framework, and flag where my instinct from [my background] would lead me to write non-idiomatic code.

Explain [concept, e.g. React hooks / async/await / goroutines] using a concrete runnable example, then deliberately show the most common beginner mistake and why it fails. I learn best from seeing the wrong version too.

I read this code in an unfamiliar codebase and don't understand it: [paste code]. Stack is [X]. Explain it line by line, define any framework-specific magic, and tell me what I'd need to know to write something similar.

Give me a focused 5-step learning path to become productive in [tool/stack] for [specific goal], skipping anything I won't need for that goal. For each step, give one small exercise I can do to confirm I've got it.

Why these work: they anchor the new stack to what you already know and ask the model to surface the mental-model differences and common traps — far faster than reading docs cold.

From single prompts to prompt chains

The prompts above are single shots, but real developer work is a chain: diagnose the bug, then write the regression test, then refactor, then document. You can run that whole sequence by hand, or you can build it once and reuse it.

That's what Studio is for. It's a visual canvas where each step — Role, Context, Instruction, Chain-of-Thought, Self-Critique — is a block you wire together, so a "debug then test then document" workflow becomes a saved, versioned chain instead of five prompts you retype every time. Add a Self-Critique block at the end and the model reviews its own output before you ever see it.

If you'd rather just get a strong answer fast, drop your rough request into the free brain — type "review this function for security issues" with your code and it returns expert-level output, no setup. And if you want to go deeper on the fundamentals first, How to Prompt covers the structure underneath all of these.

Frequently asked questions

What are the best ChatGPT prompts for developers?

The best ones paste the actual code, state your constraints (language version, framework, what you can't change), and ask the model to explain its reasoning before giving an answer. A debugging prompt should include the full stack trace; a refactoring prompt should pin behavior in place. Generic "fix my code" prompts produce generic, often wrong, fixes.

Can ChatGPT debug my code?

Yes, if you give it enough to work with. Paste the full error or stack trace, the relevant code, and what you expected versus what happened. Then ask it to trace the execution path and name the root cause before suggesting a fix — that catches bugs a one-shot answer skips. It can't see your repo, so the more real context you paste, the better.

How do I get ChatGPT to write better tests?

Tell it the test framework, then make it list the cases it'll cover before writing any code — happy path, boundaries, null and empty inputs, and error paths. Ask specifically for the cases you tend to skip when tired: off-by-one boundaries, large inputs, and regression tests for bugs you just fixed. Enumerating first produces far better coverage than "write tests for this."

Should I share proprietary code with an AI model?

Treat anything you paste as potentially leaving your control, and follow your company's policy. For sensitive code, strip secrets and identifying details, or use a tool where you bring your own key so requests run under your own provider account. Many developer prompts work fine on a representative, sanitized snippet rather than the real thing.

You don't need to memorize these — you need a place to keep the ones that work. Start with the free brain: paste your code and a rough ask, and it returns expert-level output with no setup. When a prompt earns its keep, save it as a reusable, versioned chain in Studio. And when you want a head start, the Library has 2,750+ expert prompts you can copy or open in Studio and run today.