> ## Documentation Index
> Fetch the complete documentation index at: https://bazel.build/llms.txt
> Use this file to discover all available pages before exploring further.

# AI-assisted contributions

AI assistance—including LLMs and coding assistants is welcome when contributing
to Bazel. These tools can help you navigate Bazel's codebase, explore complex
APIs, fix bugs, improve performance, and catch edge cases.

However, all contributions are reviewed by humans. When using AI tools to
assist your work, adhere to the following principles:

## Own your change

AI can help generate code, but you remain responsible for the change:

* **Understand the diff:** Do not submit code that you do not understand. You
  must be able to explain how the code works, justify architectural decisions,
  and evaluate performance or memory implications.
* **Engage in review:** You are expected to answer questions from reviewers,
  respond to feedback, and iterate on the implementation yourself.
* **Maintain quality:** Ensure all changes include adequate unit or integration
  tests and comply with Bazel's testing and style standards.

## Write for humans

Bazel maintainers and reviewers are human engineers, not automated systems.
Please respect their time:

* **Avoid walls of text:** Do not paste raw, verbose LLM explanations into PR
  descriptions, commit messages, or issue comments.
* **Focus on the why:** Keep commit messages and pull request descriptions
  concise. Explain the motivation, rationale, and user-visible impact rather than
  mechanically restating what lines changed.
* **Human communication:** Code reviews are a dialogue between people. Avoid
  automated bot responses in discussions.

## Mind the complexity

Ease of code generation does not lower the bar for architectural rigor:

* **Complexity is not free:** Every line of code, new configuration option, and
  rule attribute incurs ongoing maintenance, performance, and compatibility
  costs.
* **Exercise engineering judgment:** Maintainers will not accept features or
  abstractions simply because they are easy to produce. Focus on concrete
  problems, follow existing codebase patterns, and favor simplicity over
  unnecessary generality.


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