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Co-authored-by: Mario Loriedo <mario.loriedo@gmail.com> Signed-off-by: Jan Kaluza <hanzz.k@gmail.com>
82 lines
3.6 KiB
Markdown
82 lines
3.6 KiB
Markdown
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# Podman LLM (AI) Development Policy
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This document is based on [Jellyfin LLM Policy](https://jellyfin.org/docs/general/contributing/llm-policies/)
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and licensed under [CC-BY-ND-4.0](http://creativecommons.org/licenses/by-nd/4.0/).
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LLMs such as Claude and ChatGPT are powerful development tools. They can
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help both experienced and new developers. However, they also introduce risks.
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Podman has always prioritized code quality (readability, simplicity, and conciseness)
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and friendly communication. Our small team maintains these standards manually. As LLM
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usage grows within the Podman community, this policy clarifies our expectations for
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contributions and communication across all official projects and spaces.
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## No LLM-Generated Direct Communication
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LLM output must **not** be used verbatim in:
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* Issues or comments
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* Pull request bodies, comments, or commit messages.
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* Forum or chat posts
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* Security and vulnerability reports
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All communication must be written in your own words. You must understand what you are submitting.
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LLM-written content is often long, impersonal, and error-prone. As a small team with limited
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resources, we are unable to spend time reviewing unclear submissions or responding to impersonal
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comments.
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* Exception: If you use an LLM to translate your thoughts into English, clearly state this
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(e.g., “Translated with an LLM from MyLanguage”).
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* Exception: The LLM-based bot can be configured by the maintainers to review the PRs
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and suggest changes. These changes are only suggestions and might be wrong. It’s up
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to the contributor and maintainer to decide whether the particular suggestion makes sense.
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Repeated violations may result in the closure or deletion of the submission or in a permanent ban from Podman projects.
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## LLM Code Contributions
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LLMs may assist with code, but you are fully responsible for what you submit.
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### Requirements
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* Follow all guidelines in [CONTRIBUTING.md](CONTRIBUTING.md).
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* Keep changes concise and focused.
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* Match existing formatting and quality standards.
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* Remove unnecessary comments, poor structure, whitespace issues, and editor/LLM metadata files (e.g., .claude configs).
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* The code must build, run, and pass tests before review begins.
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* Explicitly test the functionality you modify.
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### Understanding and Ownership
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You must:
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* Review all generated code.
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* Clearly explain (in your own words) what the change does and why in both the PR body and commit message.
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* Be able to discuss and justify your changes during review.
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Submitting “vibe-coded” or poorly understood changes will result in rejection after a few attempts to correct them.
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### Handling Review Feedback
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Do not paste reviewer feedback into an LLM and resubmit whatever it generates.
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Please engage in the review process by:
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* Responding thoughtfully to feedback using your own words.
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* Making minimal, targeted changes to address comments.
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* Understanding the implementation of required changes.
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### Final Discretion
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Maintainers have final discretion. PRs that are too large, overly complex, poorly structured,
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or difficult to review may be rejected after a few attempts to correct them — regardless
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of whether LLMs were used.
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Violations may result in the closure or deletion of the submission, or in a permanent ban from Podman projects.
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## The Golden Rule
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Do not prompt an LLM vaguely. Do not commit the LLM results unchanged. And do not submit them as-is.
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Using LLMs as a tool is completely fine. Using them as a replacement for understanding,
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responsibility, and craftsmanship is not.
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