Practical Guidelines for Using AI in Coding Contributions

AI-powered coding helpers have become a normal part of day-to-day software work. They can speed up your understanding of unfamiliar repositories, help you sketch solutions fast, and support learning new patterns. For project maintainers, however, this convenience can turn into extra review effort when AI-supported pull requests arrive without enough context.

This guide lays out hands-on norms for contributors and maintainers so that AI support improves project outcomes instead of piling on review load. You’ll learn when and how to mention AI involvement, how to check your own AI-assisted changes, and how maintainers can define clear expectations in project documentation.

Key Takeaways

• Be open. Explain how AI influenced your pull request and share process context when you’re able.
• Take responsibility. Think of AI as a junior teammate. You are still accountable for tests, tricky cases, and clear explanations.
• Define norms. Maintainers should add an AI section to CONTRIBUTING.md with specific, concrete examples.
• Match disclosure to impact. Simple autocomplete doesn’t need a note. Substantial generation, debugging help, documentation, or design input does.
• Reinforce good practice. Highlight and appreciate strong, well-explained AI-assisted contributions.

Audience and Prerequisites

This guide is intended for contributors and maintainers who already know the basics of GitHub pull requests and standard review workflows.

If You’re a Contributor

State which tools you used and what you did with them

Add this near the start of your pull request description. Make the scope clear.

• Minimal: “Claude Code helped with this implementation.”
• Better: “Used ChatGPT to get oriented in the codebase. Wrote the solution myself.”
• Best: “Cursor proposed this direction; linked chat history is saved in SpecStory. I adjusted it to project conventions and added tests.”

Tip: If AI supported your comments or documentation, mention that as well. Clear notes build trust and can reduce review time.

Share your process when you can

Export your conversation from Cursor, Claude Code, or your IDE assistant and attach it to the pull request. SpecStory can preserve session context so reviewers understand how you reached your approach. That transparency often shortens review loops because maintainers can follow your logic instead of guessing it.

Own the quality and the understanding

AI should not replace your judgment.

• Read every line yourself and delete placeholders or unfinished TODOs.
• Create tests for edge scenarios, not only the happy path. If you fixed a bug, add a regression test.
• Explain the reasoning. If you can’t describe a change clearly in your own words—covering data structures, complexity, and possible failure cases—it’s not ready yet.

A simple disclosure section you can paste into your PR

AI Assistance Disclosure

• Tools: <Cursor (Claude), ChatGPT, Copilot>
• Scope: <created initial algorithm; I rewrote the IO layer; wrote all tests manually>
• Context: <link to exported chat from SpecStory or your IDE>
• Review: I checked the logic, added tests for edge cases, and verified style conventions.

If You’re a Maintainer

Set clear expectations in CONTRIBUTING.md

Add a dedicated AI Assistance section: require disclosure, explain why it matters, and show examples of good PR notes (from Minimal to Best). You can also link to a PR template checkbox such as: “Have you disclosed AI help and shared your process where relevant?”

Mitchell Hashimoto’s Ghostty repository is a solid reference for treating disclosure as a reviewer courtesy and for helping maintainers calibrate review depth based on the contributor’s explanation.

Suggested snippet:

##AI Assistance
If you used AI tools in this contribution, list them in your pull request and briefly describe the scope (docs only, debugging, partial code generation, or similar). If possible, include links to process notes or chat exports. Simple autocomplete does not need disclosure.

Review with care and speed

Look out for shifts in style, overly generic explanations, or unnecessarily complex functions. These often signal low-effort code generation.

• If something seems wrong, ask the contributor to explain a specific part and point to the tests covering it.
• It’s fine to decline changes that add more effort than value. Be explicit about what would make a future submission acceptable.

Create healthy habits

Make disclosure standard, not embarrassing. Encourage contributors to explain how AI was used, call out excellent examples in release notes, and keep a short “Responsible AI in this repo” page with preferred prompting tips and domain context (so assistants produce better output).

A Practical Guide to Disclosure

Disclosure should be mandatory when AI had a meaningful influence on the pull request, including:

• Producing or reworking non-trivial code
• Clarifying architecture, proposing design paths, or assisting debugging
• Writing substantial documentation or comments

Disclosure is not needed for:

• IDE autocomplete, syntax cleanups, or mechanical renaming
• Small inline completions that don’t change behavior

Note: If you’re unsure, disclose. It’s quick, and it prevents unnecessary back-and-forth later.

Examples You Can Reuse

Contributor pull request notes

• Minimal: “Claude Code helped with this implementation.”
• Better: “Used ChatGPT to understand the codebase. Implemented the solution manually.”
• Best: “Cursor recommended this direction; linked chat history is saved in SpecStory. I adapted it to our needs and added tests.”

Maintainer checklist

• [ ] AI disclosure included, or marked as not applicable
• [ ] Contributor can explain complex parts in their own words
• [ ] Tests cover edge cases and regressions
• [ ] Style and conventions fit the repository
• [ ] Documentation updated where behavior changes

Making It Work For Everyone

Think of AI like a junior developer: let it suggest options, while you guide, simplify, and verify. Keep in mind that a human maintainer is reviewing your pull request with limited time. Projects can make AI-assisted contributions sustainable by sharing prompting tips that work, giving short codebase overviews, and showcasing strong pull requests as examples for others.

Conclusion

AI isn’t replacing open-source contributors. It’s giving them additional ways to join in. Long-term collaboration relies on openness, respect, and careful self-review. Tools like SpecStory preserve not just the code but also the reasoning behind it, which makes reviews quicker and more helpful for everyone.

Use AI to support you, not to cut corners. Clearly state when and how it helped, verify quality yourself, and remember there are real people on the other end of your pull request. That’s how you help projects grow and stay healthy.