Add Generative AI/Low Effort Contribution Policy (#2417)

* add genai policy; closes #2400

* address feedback

* spellcheck

* add toc to top-level guide readme

* be more explicit about CoC

* address review comments

---------

Co-authored-by: Morgan McLean <[email protected]>

.cspell.yaml

@@ -1,5 +1,6 @@
 version: 0.2
-ignorePaths: ["elections/**/*", "tools/github/*", "scripts/**/*.py", "scripts/**/*.sh"]
+ignorePaths:
+  ["elections/**/*", "tools/github/*", "scripts/**/*.py", "scripts/**/*.sh"]
 # Ignore certain patterns in the sigs.yml file
 patterns:
   - name: Slack Channel ID
@@ -24,6 +25,7 @@ words:
     - Coralogix
     - DASD
     - Docu
+    - Dosu
     - datadog
     - devex
     - devstats

guides/README.md

@@ -7,4 +7,9 @@ the OpenTelemetry community. These should be considered a living resource, and
 everyone is welcome to contribute to them!
 
 - [Contributor Guide](./contributor/README.md)
-- [Maintainer Guide](./maintainer/README.md)
+  - [Policy on Generative AI Contributions](./contributor/genai.md)
+  - [Donations of pre-existing code](./contributor/donations.md)
+  - [Becoming a member of the OpenTelemetry project](./contributor/membership.md)
+  - [The Contribution Lifecycle](./contributor/processes.md)
+- [Maintainer Guide](./maintainer/README.md)
+  - [Resolving technical conflicts](./maintainer/conflict-resolution.md)

guides/contributor/genai.md

@@ -0,0 +1,97 @@
+# Generative AI Contribution Policy
+
+This policy provides general guidance for contributors and maintainers relating
+to the use of Generative AI in OpenTelemetry projects. This guidance supersedes
+and extends the policy defined in the [Linux Foundation Generative AI
+Policy](https://www.linuxfoundation.org/legal/generative-ai).
+
+## The Short Version
+
+While we welcome contributions from anyone, maintainers of individual
+projects may -- at their discretion -- hide or close issues, pull requests, or
+other contributions that are made totally or in part through generative AI
+tooling.
+
+## The Long Version
+
+Increasingly, we have observed a trend of contributors who are utilizing LLMs
+and other generative tools to participate in issues and create pull requests.
+Regurgitating the output of an LLM is unlikely to be particularly helpful, or
+valuable, to other contributors, maintainers, and end-users for a couple of reasons.
+
+First, time is a very scarce resource for maintainers and approvers. Thoughtful,
+high-quality code reviews both essential to the success of OpenTelemetry, and
+require a significant time commitment. There is not enough time to give proper
+responses to low-effort pull requests without compromising our responsiveness to
+high-effort pull requests. Second, OpenTelemetry is a complex, fast-moving
+project -- LLMs will often have stale data in their training sets, and are prone
+to offering output that is not relevant to the current state of the project.
+While LLMs can be incredibly powerful coding assistants they are not a
+substitute for human judgement and knowledge.
+
+With proper usage, Generative AI can be a valuable tool for writing code,
+documentation, tests, and more. This level of usage requires enough
+understanding of the project to evaluate the LLM output, and to know when to
+accept or reject it. Therefore, we ask that contributors do not rely on LLM
+output as the sole basis for their contributions.
+
+Examples of this include:
+
+- Copying and pasting LLM output into issues or pull requests without any
+additional context or explanation.
+- Reviewing existing pull requests solely via
+LLMs, or using LLMs to respond to issues without any additional context or
+explanation.
+
+## Frequently Asked Questions - Contributors
+
+_Q: Can I use LLMs to help me write code, documentation, or tests?_
+
+Yes, this policy does not prohibit the use of LLMs to assist in writing code,
+documentation, or tests. However, we ask that you do not rely on LLM output as
+the sole basis for your contributions.
+
+_Q: Can I use LLMs to help me review pull requests, issues, or understand the code base?_
+
+Yes, this is also allowed -- and a good idea! You should use LLMs as a tool to
+assist in your understanding, but not as a replacement for your own judgement
+and ability.
+
+_Q: How do I know the difference between allowed and disallowed usages of LLMs?_
+
+"If you have to ask, you already know the answer." This policy is not a broad
+ban of LLMs, it is a request that you -- as an individual -- use them in a way
+that adds value to the project and respects the time of other contributors and
+maintainers. If you are using LLMs to help you write code, that is fine; You
+should be clear about this in pull requests and reviews. If you are using LLMs
+to understand code so that you can participate in issues or reviews, that is
+also fine -- but you should be clear about this as well. What is not fine is
+copying and pasting a GitHub issue into an LLM prompt and asking it to write the
+PR for you, then blindly submitting that response. You must be an active and
+willing participant in the process of contributing to OpenTelemetry.
+
+## Frequently Asked Questions - Maintainers
+
+_Q: Can I close or hide issues or pull requests that are made through LLMs?_
+
+Yes, as your discretion you may close or hide issues or pull requests that are
+made through LLMs. We ask that you provide a clear explanation for why you are
+doing so, and -- if possible -- provide guidance on how the contributor can
+improve their contribution.
+
+_Q: How do I address contributors who are making consistent, low-effort contributions via LLMs?_
+
+If an individual contributor continues to engage in low-effort PRs or issues,
+_and_ you have exhausted other avenues of communication, please escalate the
+situation to the OpenTelemetry Governance Committee. Per the [Code of Conduct](https://github.com/cncf/foundation/blob/main/code-of-conduct.md),
+contributors are expected to help maintain a positive environment, which would
+include following guidance and published policy.
+
+_Q: Can I use LLM or Generative AI tooling to assist in my own work as a maintainer?_
+
+In general, you should evaluate the output of LLMs -- regardless of how you use
+them -- in the same way you'd evaluate the output of a human contributor or
+non-AI tool. For example, tools like [Dosu](https://dosu.dev/) are being used in
+certain repositories to aid in code review and issue management. Remember that
+these tools can make mistakes, and use your best judgement when evaluating their
+output.