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Greptile learns from your team’s feedback to provide increasingly relevant suggestions. The primary training methods are emoji reactions and explanatory comments.
Learning is continuous. You’ll see noticeable improvement in the first few weeks of consistent feedback, and it keeps getting better over time.

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Using Reactions (👍/👎)

Reactions are the fastest way to train Greptile. Every reaction teaches it what matters to your team.
Your ReactionWhat Greptile Learns
👍“This is useful - make more comments like this”
👎“This isn’t helpful - stop making these comments”
No reactionNeutral signal, lower priority over time
Only 👍 and 👎 train the system. Other emojis (❤️, 🚀, etc.) are treated as neutral.
For 👎 reactions, add a quick comment explaining why: 
@greptileai We don't enforce this in test files
This helps Greptile understand the context, not just that you disagreed.

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Explaining Preferences

While reactions teach what you like, comments teach why. Be specific: 
❌ "We don't do this"
✅ "We avoid wildcard imports because they hide dependencies"
Keep it short: 
❌ [Long paragraph about company history]
✅ "Webhooks must be synchronous - provider requires immediate response"

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Tracking Progress

The Analytics page in your dashboard shows how training is going:
MetricWhat it tells you
Feedback ReactionsHow consistently your team is reacting to comments
Addressed Comments per PRWhether Greptile’s suggestions are being implemented
Recent Issues CaughtTypes of issues Greptile is flagging
Low reaction counts? Remind the team to 👍/👎 comments. High “addressed” rates mean Greptile is learning what matters.

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Accelerating Learning

Instead of waiting for organic learning, you can:
  1. Upload style guides - Add your existing docs as custom context
  2. Create explicit rules - Define standards in the dashboard or greptile.json
  3. Use pattern repositories - Share learnings across repos with pattern repositories

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What’s next?