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

# Chats

> Read individual AI responses to understand what drives your aggregate metrics.

# Chats

Chat-level analysis explains why aggregate metrics move.

## Where to inspect chats

* **Overview → Recent chats**
* **Prompt detail pages** in the Prompts section

## What to inspect in a chat

* model/provider used
* prompt text
* mentioned brands and relative position
* source links and domains used
* response framing around your brand

## Why this matters

* You can catch context errors hidden by averages.
* You can identify recurring phrasing patterns.
* You can map source influence to mention quality.

## Recommended review cadence

* Spot-check daily for high-priority prompts.
* Run deeper weekly reviews for major topics.

## Common findings

* One model shifts tone before others.
* Same prompt can produce different source mixes by model.
* Mentions may exist without strong recommendation language.