AI Prompts for Coding: Copilot, Cursor, Replit and Code Review
Copy practical AI coding prompts for GitHub Copilot, Cursor, Replit, debugging, tests, refactors, architecture, reviews, security, and release prep.
Michael Okeje
Reviewed against AI coding agent and IDE workflow patterns · Last updated May 27, 2026
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How we tested this
Reviewed as a coding-agent prompt library in May 2026
We rebuilt this page around practical coding tasks that benefit from AI but still need developer review.
The prompts require context, scope, test strategy, and final verification so users do not accept plausible but broken patches.
How to use these prompts
Give the AI the exact file, stack, expected behavior, and constraints. Ask it to inspect first, change narrowly, run relevant checks, and summarize risks.
Prompt map
Workflows and starter prompts
Use these workflows to keep AI coding work small, testable, and reviewable.
| Job | Prompt focus | Starter prompt | Human check |
|---|---|---|---|
| Generate code | signature, inputs, outputs, edge cases | Write a [language] function [name] that [task]. Include types, edge cases, examples, and unit tests. | Run tests and inspect edge cases. |
| Debug | reproduction, logs, root cause, fix | Given this error and code, identify likely root cause, propose the smallest fix, and add a regression test. | Confirm the bug fails before the fix if possible. |
| Refactor | behavior unchanged, scope, tests | Refactor this file for readability without changing behavior. Keep public API stable and explain what changed. | Compare behavior and run focused tests. |
| Code review | bugs, regressions, security, tests | Review this diff for bugs, security risks, missing tests, performance regressions, and unclear API changes. | Prioritize actionable findings with file references. |
| Architecture | tradeoffs, ownership, migration path | Propose an implementation plan for [feature]. Include modules, data flow, risks, migration, and tests before writing code. | Check fit with existing architecture. |
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The reusable prompt
Generation and refactor prompts
These prompts work across Copilot, Cursor, Replit, ChatGPT, and Claude Code.
Prompt 1
Create a [function/component/API route] that does [task]. Include types, validation, error states, and tests.
Prompt 2
Refactor this code to reduce nesting and duplication without changing behavior or public API.
Prompt 3
Convert this JavaScript module to TypeScript with strict types and minimal runtime changes.
Prompt 4
Add comments only where the logic is non-obvious. Do not narrate simple assignments.
Debugging and test prompts
AI is best at debugging when you give it symptoms and proof.
Prompt 1
Use this error, stack trace, and code to identify 3 likely causes and the fastest way to test each one.
Prompt 2
Write regression tests for this bug covering normal, edge, and failure cases.
Prompt 3
Find race conditions, null handling, async errors, and permission issues in this function.
Prompt 4
Explain why this test is flaky and make it deterministic without weakening the assertion.
Review and security prompts
Use these before merging AI-assisted code.
Prompt 1
Review this diff for bugs, security, missing tests, accessibility, performance, and backwards compatibility.
Prompt 2
Threat-model this endpoint. List inputs, auth assumptions, data exposure, abuse cases, and mitigations.
Prompt 3
Create a release checklist for this feature with tests, migration, rollback, monitoring, and docs.
Prompt 4
Audit this generated code for dead code, hidden dependencies, overbroad permissions, and unsafe defaults.
What to check before accepting AI-generated code
FAQs
What is the best prompt format for AI coding?
Use stack, file context, task, constraints, expected output, tests, and verification command. Ask the tool to inspect before editing.
Which AI coding tool is best?
It depends on workflow. Copilot is strong inside IDEs, Cursor is strong for repo-aware edits, Replit helps build hosted projects, and coding agents help with larger tasks.
Can AI write production code?
Yes, but production code still needs review, tests, security checks, and understanding of the existing system.
How do I prompt AI to debug code?
Provide the error, expected behavior, actual behavior, relevant code, reproduction steps, and ask for a root-cause explanation plus a minimal fix.
Should AI write tests?
Yes. Ask for tests that fail before the fix and cover edge cases. Review whether the tests assert meaningful behavior.
What is the main AI coding prompting mistake?
The main mistake is asking for broad changes without scope, tests, or verification. That invites unrelated rewrites.