> For the complete documentation index, see [llms.txt](https://alphascan.gitbook.io/docs/llms.txt). Markdown versions of documentation pages are available by appending `.md` to page URLs; this page is available as [Markdown](https://alphascan.gitbook.io/docs/product-guide/faucet-ai.md).
# Faucet AI
AI-calculated token sentiment is our first AlphaAI feature utilizing our proprietary AI model.
The model takes into consideration a huge amount of social sentiment data we've accumulated. It provides an overview of "how hot" a token is from a sentiment perspective.
The model provides four different data points for each token specifically. The first column provides a sentiment score for the current environment. This score looks at all data in aggregate normalized for the current sentiment.
In addition, the model provides three other data points showing how sentiment has shifted over specific time periods (24h, 7D, 30D). These scores calculate if and how social sentiment has shifted in those time periods.
None of the information displayed constitutes financial advice. See our Terms of Service for details
---
# Agent Instructions
This documentation is published with GitBook. GitBook is the documentation platform designed so that both humans and AI agents can read, navigate, and reason over technical content effectively. Learn more at gitbook.com.
## 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, and the optional `goal` query parameter:
```
GET https://alphascan.gitbook.io/docs/product-guide/faucet-ai.md?ask=&goal=
```
`ask` is the immediate question: it should be specific, self-contained, and written in natural language.
`goal` is optional and describes the broader end goal you are ultimately trying to accomplish on behalf of the user. GitBook uses it to tailor the answer towards what is most useful for that goal.
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.