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

# Taming the 'PR Flood'

> Shifting AI from coder to reviewer to handle the review bottleneck

**The Problem**: As individual productivity increases (3x–10x), the volume of Pull Requests (PRs) multiplies, creating a bottleneck for human reviewers.

**The Solution**: Shift the AI's role from just a "coder" to a "reviewer" to act as an automated first line of defense.

## The "Living" Rulebook

Teams must codify tribal knowledge into a **REVIEW\_RULES.md** file in the repository root. This turns unwritten preferences into a tangible asset the AI can enforce.

### Example REVIEW\_RULES.md

```markdown theme={null}
# Code Review Rules

## Architecture
- All API endpoints must include rate limiting
- Database queries must use the query builder (no raw SQL)

## Security
- Never log sensitive data (passwords, tokens, PII)
- All user input must be validated

## Testing
- All new features require integration tests
- Test coverage must not decrease
```

## Agentic Review Workflows

Use a **REVIEW\_PROCESS.md** to define a step-by-step agentic routine for the AI, such as:

1. **Checking out branches**
2. **Triaging issues** (P0 to P2)
3. **Summarizing findings** before a human looks at the code

### Example REVIEW\_PROCESS.md

```markdown theme={null}
# Automated Review Process

## Phase 1: Setup
1. Checkout the PR branch
2. Read all modified files
3. Review the PR description

## Phase 2: Automated Checks
1. Run linter and report violations
2. Run type checker and report errors
3. Run test suite and note failures

## Phase 3: Rule Enforcement
1. Load REVIEW_RULES.md
2. Check each rule against changes
3. Categorize violations by severity:
   - P0 (Critical): Security, data integrity
   - P1 (Important): Architecture, performance
   - P2 (Minor): Style, documentation

## Phase 4: Summary
1. Generate review summary with:
   - Total issues found by priority
   - Pass/fail recommendation
   - Top 3 issues to address
```

***

**Key Takeaway**: Codify tribal knowledge into enforceable rules. AI handles the "tedious nits" so humans can focus on architecture and taste.