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AI prompts for software developers

Practical AI prompts for software engineers: code review, PR descriptions, debugging, documentation, and architecture decisions.

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Where AI genuinely saves developer time
  • Code review — catching logic errors, naming issues, missing edge cases, and security concerns in a draft before it reaches a human reviewer.
  • PR descriptions — turning a diff into a clear, structured summary that reviewers can scan in under a minute.
  • Debugging — reasoning through an error message with the relevant code to narrow down root cause before you start changing things.
  • Documentation — converting a function or module into inline docs, a README section, or an API reference from the actual code.
  • Explaining unfamiliar code — understanding a legacy module, a third-party library, or a codebase you just inherited.
What AI cannot reliably do in development
  • Guarantee correctness — AI-generated code must be read, tested, and reviewed like any other code. It can introduce subtle bugs.
  • Know your codebase — without context about your architecture, conventions, and constraints, suggestions may not fit.
  • Replace a code review — AI misses project-specific standards, team agreements, and the broader context a human reviewer brings.
  • Debug without the right code — the more relevant code and error context you provide, the better the diagnosis; vague snippets get vague answers.
How to get useful output for code tasks
  • Include the actual code, not a description of it — paste the relevant function, file, or diff rather than describing what it does.
  • State the language, framework, and version when they matter — 'TypeScript with React 19' changes what patterns are appropriate.
  • Specify what you want, not what you don't — 'write a unit test for this function' is clearer than 'help me with testing'.
  • For code review, list what to focus on — security, performance, readability, edge cases — so the model prioritizes.
  • For debugging, include the full error message and the exact line it points to, not a paraphrase.
Templates
Code review prompt
You are a senior software engineer doing a code review. Review the code below and give feedback in three sections:

**Correctness and logic** — bugs, off-by-one errors, null/undefined handling, edge cases.
**Security** — injection risks, exposed secrets, insecure defaults, trust-boundary issues.
**Clarity and maintainability** — naming, unnecessary complexity, missing comments where the code is non-obvious.

For each issue: state what it is, why it matters, and what the fix looks like. Skip sections where there is nothing to flag.

Language/framework: [e.g. TypeScript / Next.js 15]
Context: [e.g. This is a server action that handles user-submitted file uploads]

```
[Paste the code here]
```
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PR description generator
Write a clear pull request description from the diff and context below.

Structure it as:
**What this PR does** — 2-3 sentences, plain language, no jargon.
**Why** — the problem it solves or the requirement it addresses.
**How to test** — the specific steps a reviewer should take to verify it works.
**Notes** — anything the reviewer needs to know (breaking changes, follow-up tasks, known limitations).

Context: [e.g. Fixes a bug where unauthenticated users could access draft posts]
Diff or summary of changes:
[Paste the diff or a description of what was changed]
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FAQ
Should I use AI for code review instead of a human reviewer?
No — AI code review and human code review catch different things, and the two work best together. AI is fast at spotting common patterns: null handling, off-by-one errors, obvious security anti-patterns, inconsistent naming. Human reviewers catch things AI consistently misses: whether this change fits the team's architecture direction, whether the abstraction is the right one, whether a test is testing what the author thinks it is, and whether the change makes the system harder to understand for the team that will maintain it. Use AI as a pre-review step to clean up the obvious issues before a human reviewer sees the code.
How much code context should I paste into the prompt?
Enough for the model to understand what the code is doing without guessing, but not the entire codebase. For a bug or review, the relevant function plus any types or interfaces it depends on is usually sufficient. For a larger module, a summary of what it does plus the specific section you want reviewed is better than pasting everything. The key mistake is the opposite: pasting a function body without the types it uses, the error message without the relevant code, or a vague description instead of the actual code. AI diagnostic quality is proportional to the quality of the input.