ci/claude: add README.md

Signed-off-by: Ihor Solodrai <ihor.solodrai@linux.dev>

Author: theihor

ci/claude/README.md

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+# AI Code Reviews in BPF CI
+
+## TL;DR
+- **Please make sure AI is actually wrong before dismissing the review**
+  - An email response explaining why AI is wrong would be very helpful
+- BPF CI includes [a workflow](https://github.com/kernel-patches/vmtest/blob/master/.github/workflows/ai-code-review.yml) running AI code review
+- The reviews are posted as comments on [kernel-patches/bpf PRs](https://github.com/kernel-patches/bpf/pulls)
+- The review comments are forwarded to the patch recipients via email by [KPD](https://github.com/kernel-patches/kernel-patches-daemon)
+- Prompts are here: https://github.com/masoncl/review-prompts
+
+If you received an AI review for your patch submission, please try to evaluate it in the same way you would if it was written by a person, and respond.
+Your response is for humans, not for AI.
+
+## How does it work?
+
+BPF CI is processing every patch series submitted to the [Linux Kernel BPF mailing list](https://lore.kernel.org/bpf/).
+Against each patch the system executes various tests, such as [selftests/bpf](https://git.kernel.org/pub/scm/linux/kernel/git/bpf/bpf-next.git/tree/tools/testing/selftests/bpf), and since recently it also executes automated code reviews performed by LLM-based AI.
+
+BPF CI runs on [Github Actions](https://docs.github.com/en/actions) workflows orchestrated by [KPD](https://github.com/kernel-patches/kernel-patches-daemon).
+
+The AI review is implemented with [Claude Code GitHub Action](https://github.com/anthropics/claude-code-action), which essentially installs Claude Code command-line app and a MCP server with a number of common tools available to it.
+
+LLMs are accessed via [AWS Bedrock](https://aws.amazon.com/bedrock), the GitHub Actions workflow authenticates to AWS account with [OIDC](https://docs.github.com/en/actions/how-tos/secure-your-work/security-harden-deployments/oidc-in-aws).
+
+To achieve the output that might have led you to this page, [a set of elaborate prompts](https://github.com/masoncl/review-prompts) were developed specifically targeting the Linux Kernel source code.
+The workflow checks out the Linux and prompts repository and initiates the review with a trivial [trigger prompt](https://github.com/kernel-patches/vmtest/blob/master/.github/workflows/ai-code-review.yml#L91-L94).
+
+### Are the reviews even accurate?
+
+We make every effort for AI reviews to be high-signal messages. Although the nature of LLMs makes them prone to mistakes.
+
+At this point this is still an experiment, but the results so far have been promising.
+For example, AI is pretty good at catching dumb mistakes (e.g. use-after-free) that humans can easily miss.
+At the same time AI can miss context obvious to a human, such as relationships between newer and older changes.
+
+If you'd like to suggest an improvement to the prompts, open a PR to [review-prompts](https://github.com/masoncl/review-prompts) repository.
+
+### Will my patch get nacked because of the AI review?
+
+Paraphrasing IBM training manual:
+> "A LLM can never be held accountable, therefore a LLM must never make an Ack/Nack decision"
+
+The review prompts are designed such that AI is only searching for the regressions it can provide evidence for.
+For the majority of patches a review is not generated, so if you received one it's worth evaluating.
+
+It's unlikely that your patch gets discarded *just* because AI found something, especially if you address it or explain why AI is wrong.
+
+But if you ignore an AI review, human reviewers will likely ask for a reason.
+
+### What if I don't like it?
+
+Bring it up with the maintainers on the mailing list and elaborate.
+
+It is expected that AI may be mistaken. However it is also expected that the patch authors answer reasonable questions about the code changes they propose.
+
+If there is a technical issue (say with email notifications, formatting etc.), open an issue in [this repository](https://github.com/kernel-patches/vmtest/issues).
+
+### Who pays for the tokens?
+
+[Meta Platforms, Inc.](https://www.meta.com/)
+
+BPF CI in its current form has been developed and maintained by the Linux Kernel team at Meta. Most of the relevant hardware is also provided by Meta.
+
+### Who set this up?
+
+- [Chris Mason](https://github.com/masoncl) is the prompt engineer
+- [Ihor Solodrai](https://github.com/theihor) is the infra plumber