LLVM Adopts 'Human In The Loop' Policy For AI/Tool-Assisted Contributions

The LLVM compiler infrastructure project has officially codified its stance on AI-assisted contributions with a new "human in the loop" policy, establishing clear guidelines for how developers can use large language models and other automated tools when submitting code to the project.

The policy, which was formalized following extensive community discussions in 2025, directly addresses a growing problem that many open-source projects are now facing: a surge in low-quality, AI-generated contributions that waste maintainer time without providing genuine value.

The Core Principle: Human Accountability

At its heart, the LLVM policy establishes that while contributors are free to use any tools they wish—including advanced code assistants like GitHub Copilot, Cursor, or custom LLM pipelines—they must remain fully accountable for their submissions. The project's documentation makes this explicit: "The contributor is always the author and is fully accountable for their contributions."

This means that every line of code, every documentation patch, and every bug fix submitted to LLVM must be thoroughly reviewed and understood by a human developer before being presented to the community. Contributors must be able to answer questions about their work during code review and demonstrate that they understand the changes they're proposing.

Addressing the "LLM Garbage" Problem

The policy directly references the practical challenges that prompted its creation. As stated in the official announcement: "Over the course of 2025, we observed an increase in the volume of LLM-assisted nuisance contributions to the project. Nuisance contributions have always been an issue for open-source projects, but until LLMs, we made do without a formal policy banning such contributions."

This isn't just theoretical. Many maintainers of large open-source projects have reported receiving pull requests that contain obviously generated code—code that compiles but doesn't solve the stated problem, code that introduces subtle bugs, or patches that are clearly copy-pasted from AI responses without proper context or understanding.

The LLVM project's solution isn't to ban AI tools outright, but to enforce a quality gate: if you use an AI assistant to help write code, you must understand that code well enough to defend it in review and take responsibility for it.

Practical Implementation: Transparency and Labeling

The policy includes specific requirements for transparency. Contributors must label contributions that contain substantial amounts of tool-generated content. The project suggests using commit message trailers like Assisted-by: [name of code assistant] to indicate when AI tools were used.

This serves multiple purposes:

1. It helps reviewers understand the context and potential limitations of the contribution
2. It builds community knowledge about how these tools are being used in practice
3. It creates accountability by making tool usage visible to everyone

The policy explicitly states that this labeling is "intended to facilitate reviews, and not to track which parts of LLVM are generated." The goal isn't surveillance, but better collaboration.

Guidance for New Contributors

Recognizing that new developers might be tempted to use AI tools to accelerate their learning, LLVM provides specific guidance: "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."

This is a crucial distinction. The project wants to encourage learning and growth, but it emphasizes that "Passing maintainer feedback to an LLM doesn't help anyone grow, and does not sustain our community."

The message is clear: AI tools can be helpful for learning syntax or exploring ideas, but they shouldn't be used as a substitute for genuine understanding. The project aspires to be "a welcoming community that helps new contributors grow their expertise," but that growth requires "taking small steps, getting feedback, and iterating."

Context: A Broader Industry Trend

LLVM's policy doesn't exist in isolation. It's part of a broader conversation happening across the open-source ecosystem. The Linux kernel community, for instance, has been grappling with similar questions, with maintainers expressing concerns about AI-generated patches that lack proper context or understanding.

Other major projects are also developing their own approaches. Some have taken stricter stances, while others are still figuring out how to balance the potential benefits of AI assistance with the risks of low-quality contributions.

What makes LLVM's approach notable is its balance: it doesn't ban AI tools, but it doesn't allow unchecked AI contributions either. It places the responsibility squarely on the human contributor to understand and validate their work.

Technical Implications for Compiler Development

For LLVM specifically, this policy has particular significance. Compiler development is a highly specialized field where small changes can have far-reaching implications for performance, correctness, and security. A subtle bug in a compiler optimization pass could affect millions of programs, and understanding the intricate details of compiler internals requires significant expertise.

The policy implicitly acknowledges that while AI tools might help with boilerplate code or simple fixes, they're not yet capable of the deep, nuanced understanding required for compiler development. The requirement that contributors "be able to answer questions about their work" during review ensures that human expertise remains central to the process.

Looking Forward

As AI tools continue to evolve and become more capable, the LLVM policy will likely need to adapt. The project has created a framework that can accommodate better tools while maintaining its quality standards.

For now, the message to the community is clear: use whatever tools help you be more productive, but never abdicate your responsibility as a contributor. The human in the loop isn't just a policy requirement—it's a recognition that building great software, especially complex systems like compilers, still requires human judgment, understanding, and accountability.

The full policy is available in the LLVM project's documentation, and the community discussion continues on the project's mailing lists and issue trackers.