Quick Overview

• 👍 Thumbs up: Mesrai learns what your team likes
• 👎 Thumbs down: Mesrai learns what to avoid
• ✨ Auto-learning: Mesrai tracks which suggestions get implemented

How the Learning Works

1. Collecting Feedback

Mesrai learns from three main sources:

• Direct Reactions: Team members can react to Mesrai’s suggestions on any code management tool:

  • 👍 = “This is what we want”
  • 👎 = “This doesn’t match our style”

• Implementation Tracking: When your team implements Mesrai’s suggestions, she marks it as positive feedback

• Pattern Recognition: Mesrai builds knowledge clusters based on your team’s preferences

2. Smart Filtering

Once Mesrai has enough data, she:

1. Analyzes new code suggestions
2. Compares them against your team’s preference clusters
3. Only shows suggestions that match your team’s style
4. Filters out suggestions similar to previously rejected ones

Why Your Feedback Matters

The more feedback you provide, the better Mesrai becomes at:

• ✅ Matching your coding style
• 🎯 Making relevant suggestions
• 🚫 Avoiding unwanted patterns
• 💡 Understanding team preferences

Best Practices

1. Be Consistent: Encourage the team to provide feedback regularly
2. React Promptly: Give feedback while the context is fresh
3. Team Alignment: Ensure the team agrees on what makes a good suggestion