# Chat Analysis

The Chat Summary function, powered by [Fractal](/fractal.md), enhances communication efficiency by providing concise summaries of past discussions within chat platforms.

Add the [AI Playground](/research/ai-playground.md) as an admin in your chat and use the `/summarize` command. You can even provide a prompt after `/summarize.`

## Examples

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<div><figure><img src="/files/NHqdOCEBloizdkDO6OJS" alt=""><figcaption></figcaption></figure> <figure><img src="/files/TFpdkin2kHKsEMrUwH7A" alt=""><figcaption></figcaption></figure></div>

## **Features & Benefits**

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* **Capacity**: Currently capable of summarizing up to 1000 past messages, the bot offers tailored summaries based on [user tier](/guides/user-tiers.md) permissions.
* **Memory**: It also retains past summaries to track trends and narrative evolution within the chat, offering a comprehensive understanding over time.
* **Privacy**: The bot starts collecting data only after it has been added to the chat, ensuring privacy and relevance in the data it accesses and summarizes.
* **Efficiency**: Quickly catch up on past discussions without manually scrolling through hundreds of messages. Keeps users informed about the latest developments and discussion shifts without constant monitoring.
* **Clarity**: Provides clear and actionable insights by distilling extended conversations into essential information.

## **Practical Applications**

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* **For Professionals**: Enables business professionals to stay updated with essential communications without dedicating extensive time to read through every chat.
* **Community Managers**: Assists in monitoring community engagement and sentiment efficiently.
* **Rewards**: By integrating chat summaries with scoring systems and leaderboards, projects can reward active and helpful community members. This can be done by analyzing chat contributions quantitatively and qualitatively, assigning scores based on the value and frequency of contributions, and fostering a vibrant and collaborative community environment.


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# Agent Instructions: Querying This Documentation

If you need additional information that is not directly available in this page, you can query the documentation dynamically by asking a question.

Perform an HTTP GET request on the current page URL with the `ask` query parameter:

```
GET https://docs.alphakek.ai/chat-analysis.md?ask=<question>
```

The question should be specific, self-contained, and written in natural language.
The response will contain a direct answer to the question and relevant excerpts and sources from the documentation.

Use this mechanism when the answer is not explicitly present in the current page, you need clarification or additional context, or you want to retrieve related documentation sections.