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# AI Policy

> **TL;DR** — AI tools assist our workflow at every stage. Humans remain in control of every decision, every review, and every release.

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## Overview

This document describes how artificial intelligence tools are used in the maintenance and development of this project. It is intended to be transparent with our contributors, users, and the broader open-source community about the role AI plays — an...

We believe in honest, clear communication about AI-assisted workflows. This policy will be updated as our practices evolve.

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## Our Guiding Principle

**AI assists. Humans decide.**

The maintainers who have been stewarding this project for years remain fully responsible for every line of code that ships. AI tools extend our capacity to review, research, and improve — they do not replace human judgment, expertise, or accountabi...

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## How AI Is Used in This Project

### 1. Code and Issue Analysis

AI tools help us process and understand incoming issues, pull requests, and code changes at scale. This includes:

- Summarising issue reports and identifying patterns across similar bugs
- Analysing code diffs for potential problems, regressions, or style inconsistencies
- Surfacing relevant context from the codebase, documentation, and prior discussions
- Flagging potential security concerns for human review

This analysis is **always** used as input to human decision-making, never as a substitute for it.

### 2. Draft Pull Requests

AI may generate draft pull requests as a starting point for a fix, a refactor, or an improvement. These drafts:

- Are clearly labelled as AI-generated when created
- Represent a first pass only — they are never considered complete or correct without human review
- May be substantially reworked, rejected, or replaced entirely by maintainers

Think of these drafts the way you would think of a junior contributor's first attempt: useful raw material that still needs experienced eyes.

### 3. Human Review of Every Pull Request

**Every pull request — whether AI-drafted or human-authored — is reviewed by a human maintainer before it can be merged.**

During review, maintainers actively use AI as a tool to assist their own thinking:

- Asking AI to explain or justify specific implementation choices
- Challenging AI-generated code and requesting alternative approaches
- Using AI to research edge cases, relevant standards, or upstream behaviour
- Requesting targeted rewrites of individual sections based on review feedback

The maintainer's judgment always takes precedence. AI answers are treated as input to be verified, not conclusions to be accepted.

### 4. Test Coverage and Defect Detection

AI helps us improve the quality and completeness of our test suite by:

- Suggesting test cases for edge conditions and failure modes
- Identifying gaps in existing test coverage
- Proposing tests that target known classes of defects or security issues
- Helping reproduce and characterise reported bugs

All suggested tests are reviewed and validated by maintainers before being committed.