## Model Proposer
* Conceptualise and introduce new models
* Delineate clear use cases and pinpoint relevant data sources that underpin development and efficacy
## Data Contributor
* Offer valuable datasets that help the model to learn and adapt
## End User
* Offer critical user feedback and insights that drive improvment of models
## Developer
* Act as the bridge between the model's capabilities and practical applications
* Leverage the API endpoints to integrate the model into diverse software applications, thereby expanding its functional reach and accessibility.
At the core of this ecosystem lies a sophisticated chatbot, driven by state-of-the-art LLMs.
The chatbot is not only empowered by the computational prowess of LLMs but is also equipped with advanced search and retrieval functionalities. This enables it to extract and leverage information from a dynamically evolving database—a repository enriched by the continuous contributions of Data Contributors.
This dual capability ensures that the chatbot remains both intelligent and informed, able to deliver nuanced and contextually relevant responses.
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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.flock.io/flock-products/ai-marketplace/participants.md?ask=
```
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.
