Training the Learning System

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.

Using Reactions (👍/👎)

Reactions are the fastest way to train Greptile. Every reaction teaches it what matters to your team.

Your Reaction	What Greptile Learns
👍	“This is useful - make more comments like this”
👎	“This isn’t helpful - stop making these comments”
No reaction	Neutral 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.

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"

Tracking Progress

The Analytics dashboard shows how training is going:

Metric	What it tells you
Addressed rate	Whether Greptile’s suggestions are being implemented
Upvote/Downvote ratio	How consistently your team is reacting to comments
Critical bugs caught	Types of issues Greptile is flagging

Low upvote counts? Remind the team to 👍/👎 comments. High addressed rates mean Greptile is learning what matters.

Accelerating Learning

Instead of waiting for organic learning, you can:

Upload style guides - Add your existing docs as custom context
Create explicit rules - Define standards in the dashboard, .greptile/ config, or greptile.json
Use cross-repo context - Share related repository context with repo clusters

What’s next?

⌘I

Getting Started

Code Review

Self-Hosting

Account

How Greptile Works

Training the Learning System

Using Reactions (👍/👎)

Explaining Preferences

Tracking Progress

Accelerating Learning

What’s next?

​Using Reactions (👍/👎)

​Explaining Preferences

​Tracking Progress

​Accelerating Learning

​What’s next?

Using Reactions (👍/👎)

Explaining Preferences

Tracking Progress

Accelerating Learning

What’s next?