CodeRabbit Core Workflow B: Learnings & Tuning

Overview

After initial CodeRabbit setup (Workflow A), this skill covers tuning review quality through learnings, code guidelines, tone customization, and noise reduction. CodeRabbit improves over time by learning from your team's feedback patterns and custom rules.

Prerequisites

• CodeRabbit installed and producing reviews (see coderabbit-core-workflow-a)
• Several PRs worth of review history
• Understanding of team coding standards

Instructions

Step 1: Configure Code Guidelines

CodeRabbit automatically detects coding rules from standard config files in your repo. It also reads AI agent configuration files for additional context.

# Files CodeRabbit auto-detects for coding rules:
# - .eslintrc.* / eslint.config.*     (JavaScript/TypeScript rules)
# - .prettierrc / prettier.config.*   (Formatting rules)
# - biome.json / biome.jsonc          (Biome linter rules)
# - .cursorrules                      (Cursor AI rules)
# - CLAUDE.md                         (Claude Code instructions)
# - .editorconfig                     (Editor settings)
# - .rubocop.yml                      (Ruby style)
# - ruff.toml / pyproject.toml        (Python rules)

# Add custom guidelines file:
# Create docs/CODING_STANDARDS.md with your team's rules
# Then reference it in .coderabbit.yaml:

# .coderabbit.yaml - Custom code guidelines
reviews:
  knowledge_base:
    code_guidelines:
      auto_detection: true          # Auto-detect from config files
      custom_patterns:
        - "docs/CODING_STANDARDS.md"
        - "docs/SECURITY_POLICY.md"
        - "team/code-style.txt"

Step 2: Train with Learnings via PR Feedback

Learnings are enabled by default. CodeRabbit learns from your team's review interactions:

# When CodeRabbit gives feedback you disagree with, reply:
"We intentionally use default exports in this project for Next.js pages.
Please don't flag default exports in files under src/pages/."

# CodeRabbit remembers this preference for future reviews.

# When you want to reinforce a pattern, reply positively:
"Good catch! We always want to flag missing error boundaries in React components."

# View current learnings in the CodeRabbit dashboard:
# app.coderabbit.ai > Organization > Learnings