Memory and Learning

Greptile’s memory system learns from every interaction with your team to deliver increasingly personalized and actionable code review suggestions.

​

How Greptile Learns From Your Team

​

1. Reading Team Comments on PRs

Greptile observes patterns in your team’s code review discussions:

Examples of Learning:

Team consistently comments: "Add error handling"
→ Greptile learns: Error handling is important to this team

Team often says: "This should be async"
→ Greptile learns: Team prefers async patterns

Team flags: "Move this to a service layer"
→ Greptile learns: Team follows layered architecture

​

2. Learning from Replies to Greptile

Your responses teach Greptile what matters:

Greptile: "Consider extracting this logic into a utility function"
Developer: "Good catch! Will refactor this."
→ Greptile learns: Code organization suggestions are valued

Greptile: "Consider extracting this logic into a utility function"
Developer: "Good catch! Will refactor this."
→ Greptile learns: Code organization suggestions are valued

Greptile: "This function is quite long"
Developer: "In our domain layer, we prefer detailed functions for clarity"
→ Greptile learns: Length rules don't apply to domain logic

Greptile: "Consider adding JSDoc comments"
Developer: "We don't document internal utilities"
→ Greptile learns: Documentation rules vary by code type

​

3. Learning from Reactions

Thumbs up/down reactions provide instant feedback on suggestion quality:

​

Learning Nitpickiness Levels

Greptile learns your team’s tolerance for minor suggestions through commit analysis and reactions:

​

Commit-Based Learning

Greptile analyzes which comments get addressed by comparing first and last commits:

​

Adaptive Noise Filtering

High Nitpick Team (addresses style issues):

✅ Missing semicolons
✅ Import organization  
✅ Function naming
✅ Documentation gaps

Low Nitpick Team (ignores style issues):

❌ Missing semicolons (suppressed after 3 ignores)
❌ Import organization (team doesn't care)
✅ Security issues (always flagged)
✅ Logic errors (never suppressed)

​

Learning Thresholds

// Greptile tracks patterns like:
const learningData = {
  semicolonComments: { made: 10, addressed: 0, reactions: -3 },
  securityComments: { made: 5, addressed: 5, reactions: +4 },
  performanceComments: { made: 8, addressed: 6, reactions: +2 }
};

// Result: Stop semicolon comments, prioritize security

​

Impact of Learning and Memory

​

More Actionable Comments

Learning transforms generic suggestions into targeted, team-specific guidance:

Before Learning (Generic):

🤖 "Consider adding error handling"
🤖 "This function could be shorter"  
🤖 "Add documentation here"
🤖 "Fix indentation"

After Learning (Personalized):

🤖 "Add error handling using your team's Result<T> pattern"
🤖 "Consider breaking this into multiple domain methods (per your architecture)"
🤖 "Security validation missing - required for payment functions"

​

Contextual Understanding

Greptile learns when rules apply and when they don’t:

// Greptile learns these patterns:
class PaymentService {
  // ✅ Long functions OK in domain logic
  processComplexPayment(data: PaymentData) {
    // 50+ lines of business logic - team accepts this
  }
}

// ❌ But flags long functions in utilities
function formatString(input: string) {
  // 20+ lines here would get flagged
}

// Greptile learns these patterns:
class PaymentService {
  // ✅ Long functions OK in domain logic
  processComplexPayment(data: PaymentData) {
    // 50+ lines of business logic - team accepts this
  }
}

// ❌ But flags long functions in utilities
function formatString(input: string) {
  // 20+ lines here would get flagged
}

# Team A: Prefers explicit error handling
def transfer_funds(amount, account):
    try:
        # explicit try/catch
    except Exception as e:
        # handle errors

# Team B: Prefers Result objects  
def transfer_funds(amount, account) -> Result[Transfer]:
    # return Success() or Failure()

​

Reduced Review Fatigue

Memory eliminates noise and focuses on what matters:

Measurable Impact:

• 80% reduction in ignored comments
• 3x higher suggestion adoption rate
• Faster PR review cycles
• Focus on architecture and logic over style

​

Custom Rules Discovery

Greptile automatically infers custom rules from team behavior without manual configuration:

​

Auto-Generated Rules

From Team Comments:

Observed pattern: Team always comments "Move DB calls to service layer"
→ Auto-generated rule: "Controllers should not contain direct database calls"

Observed pattern: Team consistently requests "Add input validation"  
→ Auto-generated rule: "API endpoints require input validation"

​

Learning Evolution

Evolution Timeline:

• Week 1-2: Standard suggestions, high noise
• Week 3-4: Learning team preferences, filtering begins
• Week 5-8: Custom patterns emerge, suggestions improve
• Week 9+: Highly personalized, actionable recommendations

​

Real-World Learning Examples

​

Team A: Security-Focused Fintech

Learning Journey:

Month 1: Generic security suggestions ignored
Month 2: Team comments "We use our custom auth middleware"  
Month 3: Greptile learns to suggest team's auth patterns
Result: 90% suggestion adoption rate for security issues

​

Team B: Performance-Obsessed Gaming

Learning Journey:

Week 1: Style comments get 👎 reactions
Week 3: Performance comments get 👍 reactions
Week 6: Greptile stops style suggestions, focuses on performance
Result: Faster reviews, better performance optimization

​

Why Learning and Memory Matter

​

The Learning Advantage

Traditional static analysis tools give the same generic suggestions to every team. Greptile’s memory system creates a personalized code review experience that:

• Adapts to your team’s coding style and preferences
• Learns from every interaction and piece of feedback
• Evolves to become more valuable over time
• Focuses on issues that actually matter to your team

The result is an AI code reviewer that feels like a knowledgeable teammate who understands your codebase, respects your decisions, and helps you write better code without the noise.