Kody Learning
Reinforcement Learning
Kody gets smarter with every interaction. By learning from your team’s feedback, she continuously adapts her code suggestions to match your team’s preferences and standards.
Quick Overview
- 👍 Thumbs up: Kody learns what your team likes
- 👎 Thumbs down: Kody learns what to avoid
- ✨ Auto-learning: Kody tracks which suggestions get implemented
How the Learning Works
1. Collecting Feedback
Kody learns from three main sources:-
Direct Reactions: Team members can react to Kody’s suggestions on any code management tool:
- 👍 = “This is what we want”
- 👎 = “This doesn’t match our style”
- Implementation Tracking: When your team implements Kody’s suggestions, she marks it as positive feedback
- Pattern Recognition: Kody builds knowledge clusters based on your team’s preferences
2. Smart Filtering
Once Kody has enough data, she:- Analyzes new code suggestions
- Compares them against your team’s preference clusters
- Only shows suggestions that match your team’s style
- Filters out suggestions similar to previously rejected ones
Why Your Feedback Matters
The more feedback you provide, the better Kody becomes at:- ✅ Matching your coding style
- 🎯 Making relevant suggestions
- 🚫 Avoiding unwanted patterns
- 💡 Understanding team preferences
Best Practices
- Be Consistent: Encourage the team to provide feedback regularly
- React Promptly: Give feedback while the context is fresh
- Team Alignment: Ensure the team agrees on what makes a good suggestion