Greptile learns to eliminate nitpicky comments that distract developers from critical issues by analyzing which suggestions your team actually acts on.

​
The Nitpick Problem

Nitpicks are minor suggestions that distract from critical issues: Problem: Developers focus on easy-to-fix style issues while missing critical bugs.

​
How Greptile Learns to Filter Noise

​
1. Commit Analysis

Greptile reads the first and last commit of every PR to see which comments were addressed:

​
2. Learning from Reactions

Thumbs up/down reactions provide immediate feedback: 
Greptile: "Consider adding error handling here"
Developer: 👍 (implements the suggestion)
→ Greptile learns: "Error handling suggestions are valuable"

​
3. Learning Examples

Example 1: Style Comments Get Filtered Example 2: Critical Issues Always Surface 
// Greptile will ALWAYS comment on this (even if team ignores style):
function transferMoney(amount, account) {
  // No input validation - SECURITY RISK
  database.transfer(amount, account);
}

// But stops commenting on this after learning:
const user = getUser()  // Missing semicolon - but team doesn't care

​
Learning Thresholds

Greptile stops making certain types of comments based on team behavior:

​
What Gets Suppressed vs. What Doesn’t

​
The Result: Focused Reviews

Before Learning: 
PR #123 - Add user authentication
├── 🔴 Missing input validation (CRITICAL)
├── 🟡 Consider adding error handling
├── 🟢 Missing semicolon on line 45
├── 🟢 Inconsistent indentation
├── 🟢 Import order could be improved  
├── 🟢 Function could use better name
└── 🟢 Missing JSDoc comment
After Learning (Team ignores style issues): 
PR #123 - Add user authentication  
├── 🔴 Missing input validation (CRITICAL)
└── 🟡 Consider adding error handling

​
Why This Matters

Reduced Noise

Fewer distracting comments mean developers focus on what matters

Faster Reviews

Less time spent on trivial issues, more on logic and architecture

Better Adoption

Teams are more likely to act on focused, relevant suggestions

Adaptive AI

The system becomes more valuable over time as it learns your preferences