LLVM AI Tool Use Policy — LLVM 23.1.0 documentation

# LLVM AI Tool Use Policy

## Policy

LLVM’s policy is that contributors can use whatever tools they would like to craft their contributions, but there must be a human in the loop. Contributors must read and review all LLM-generated code or text before they ask other project members to review it. The contributor is always the author and is fully accountable for their contributions. Contributors should be sufficiently confident that the contribution is high enough quality that asking for a review is a good use of scarce maintainer time, and they should be able to answer questions about their work during review.

We expect that new contributors will be less confident in their contributions, and our guidance to them is to start with small contributions that they can fully understand to build confidence. We aspire to be a welcoming community that helps new contributors grow their expertise, but learning involves taking small steps, getting feedback, and iterating. Passing maintainer feedback to an LLM doesn’t help anyone grow, and does not sustain our community.

Contributors are expected to be transparent and label contributions that contain substantial amounts of tool-generated content. Our policy on labelling is intended to facilitate reviews, and not to track which parts of LLVM are generated. Contributors should note tool usage in their pull request description, commit message, or wherever authorship is normally indicated for the work. For instance, use a commit message trailer like Assisted-by: . This transparency helps the community develop best practices and understand the role of these new tools.

This policy includes, but is not limited to, the following kinds of contributions:

- Code, usually in the form of a pull request

- RFCs or design proposals

- Issues or security vulnerabilities

- Comments and feedback on pull requests

## Details

To ensure sufficient self review and understanding of the work, it is strongly recommended that contributors write PR descriptions themselves (if needed, using tools for translation or copy-editing). The description should explain the motivation, implementation approach, expected impact, and any open questions or uncertainties to the same extent as a contribution made without tool assistance.

An important implication of this policy is that it bans agents that take action in our digital spaces without human approval, such as the GitHub `@claude` agent. Similarly, automated review tools that publish comments without human review are not allowed. However, an opt-in review tool that keeps a human in the loop is acceptable under this policy. As another example, using an LLM to generate documentation, which a contributor manually reviews for correctness, edits, and then posts as a PR, is an approved use of tools under this policy.

AI tools must not be used to fix GitHub issues labelled `good first issue`. These issues are generally not urgent, and are intended to be learning opportunities for new contributors to get familiar with the codebase. Whether you are a newcomer or not, fully automating the process of fixing this issue squanders the learning opportunity and doesn’t add much value to the project. Using AI tools to fix issues labelled as “good first issues” is forbidden.

## Extractive Contributions

The reason for our “human-in-the-loop” contribution policy is that processing patches, PRs, RFCs, and comments to LLVM is not free – it takes a lot of maintainer time and energy to review those contributions! Sending the unreviewed output of an LLM to open source project maintainers extracts work from them in the form of design and code review, so we call this kind of contribution an “extractive contribution”.

Our golden rule is that a contribution should be worth more to the project than the time it takes to review it. These ideas are captured by this quote from the book Working in Public by Nadia Eghbal:

> “When attention is being appropriated, producers need to weigh the costs and benefits of the transaction. To assess whether the appropriation of attention is net-positive, it’s useful to distinguish between extractive and non-extractive contributions. Extractive contributions are those where the marginal cost of reviewing and merging that contribution is greater than the marginal benefit to the project’s producers. In the case of a code contribution, it might be a pull request that’s too complex or unwieldy to review, given the potential upside.” – Nadia Eghbal

Prior to the advent of LLMs, open source project maintainers would often review any and all changes sent to the project simply because posting a change for review was a sign of interest from a potential long-term contributor. While new tools enable more development, it shifts effort from the implementor to the reviewer, and our policy exists to ensure that we value and do not squander maintainer time.

Reviewing changes from new contributors is part of growing the next generation of contributors and sustaining the project. We want the LLVM project to be welcoming and open to aspiring compiler engineers who are willing to invest time and effort to learn and grow, because growing our contributor base and recruiting new maintainers helps sustain the project over the long term. Being open to contributions and liberally granting commit access is a big part of how LLVM has grown and successfully been adopted all across the industry. We therefore automatically post a greeting comment to pull requests from new contributors and encourage maintainers to spend their time to help new contributors learn.

## Handling Violations

If a maintainer judges that a contribution doesn’t comply with this policy, they should paste the following response to request changes:

    This PR doesn't appear to comply with our policy on tool-generated content,
    and requires additional justification for why it is valuable enough to the
    project for us to review it. Please see our developer policy on
    AI-generated contributions: http://llvm.org/docs/AIToolPolicy.html

The best ways to make a change less extractive and more valuable are to reduce its size or complexity or to increase its usefulness to the community. These factors are impossible to weigh objectively, and our project policy leaves this determination up to the maintainers of the project, i.e. those who are doing the work of sustaining the project.

If or when it becomes clear that a GitHub issue or PR is off-track and not moving in the right direction, maintainers should apply the `extractive` label to help other reviewers prioritize their review time.

If a contributor fails to make their change meaningfully less extractive, maintainers should escalate to the relevant moderation or admin team for the space (GitHub, Discourse, Discord, etc) to lock the conversation.

## Copyright

Artificial intelligence systems raise many questions around copyright that have yet to be answered. Our policy on AI tools is similar to our copyright policy: Contributors are responsible for ensuring that they have the right to contribute code under the terms of our license, typically meaning that either they, their employer, or their collaborators hold the copyright. Using AI tools to regenerate copyrighted material does not remove the copyright, and contributors are responsible for ensuring that such material does not appear in their contributions. Contributions found to violate this policy will be removed just like any other offending contribution.

## Exceptions

We have one exception to this policy for the Bazel-fixer bot. The project council approved this RFC proposing to use a combination of dwyu and LLMs to maintain the Bazel build files.

Any future exception will be considered individually on its own merits as to whether it is useful to the project or extracts work from maintainers.

## Examples

Here are some examples of contributions that demonstrate how to apply the principles of this policy:

- This PR contains a proof from Alive2, which is a strong signal of value and correctness.

- This generated documentation was reviewed for correctness by a human before being posted.

## References

Our policy was informed by experiences in other communities:

- Fedora Council Policy Proposal: Policy on AI-Assisted Contributions (fetched 2025-10-01): Some of the text above was copied from the Fedora project policy proposal, which is licensed under the Creative Commons Attribution 4.0 International License. This link serves as attribution.

- Rust draft policy on burdensome PRs

- Seth Larson’s post on slop security reports in the Python ecosystem

- The METR paper Measuring the Impact of Early-2025 AI on Experienced Open-Source Developer Productivity.

- QEMU bans use of AI content generators

- Slop is the new name for unwanted AI-generated content