> ## Documentation Index
> Fetch the complete documentation index at: https://www.greptile.com/docs/llms.txt
> Use this file to discover all available pages before exploring further.

# Training the Learning System

> Train Greptile with emoji reactions and feedback to improve code review relevance. Learn how thumbs up/down signals teach what matters to your team.

Greptile learns from your team's feedback to provide increasingly relevant suggestions. The primary training methods are emoji reactions and explanatory comments.

<Note>
  Learning is continuous. You'll see noticeable improvement in the first few weeks of consistent feedback, and it keeps getting better over time.
</Note>

## 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          |

<Note>
  Only 👍 and 👎 train the system. Other emojis (❤️, 🚀, etc.) are treated as neutral.
</Note>

**For 👎 reactions**, add a quick comment explaining why:

```text theme={}
@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:**

```text theme={}
❌ "We don't do this"
✅ "We avoid wildcard imports because they hide dependencies"
```

**Keep it short:**

```text theme={}
❌ [Long paragraph about company history]
✅ "Webhooks must be synchronous - provider requires immediate response"
```

## Tracking Progress

The [Analytics dashboard](/analytics) 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:

1. **Upload style guides** - Add your existing docs as [custom context](/code-review/custom-standards)
2. **Create explicit rules** - Define standards in the dashboard, [`.greptile/` config](/code-review/greptile-config), or `greptile.json`
3. **Use cross-repo context** - Share related repository context with [repo clusters](/code-review/cross-repo-context)

## What's next?

* [Control nitpickiness →](/code-review/controlling-nitpickiness)
* [Add custom standards →](/code-review/custom-standards)
* [Configure with .greptile/ →](/code-review/greptile-config)
* [greptile.json reference →](/code-review/greptile-json-reference)