> For the complete documentation index, see [llms.txt](https://docs.predicthq.com/llms.txt). Markdown versions of documentation pages are available by appending `.md` to page URLs; this page is available as [Markdown](https://docs.predicthq.com/ai.md).

# AI & agents

- [Build with AI](https://docs.predicthq.com/ai/build-with-ai.md): Ground AI systems in verified real-world event data and build integrations faster with the MCP server, agent skills, and AI-readable docs.
- [Grounding LLMs in real-world event data](https://docs.predicthq.com/ai/grounding-llms-in-real-world-data.md): What grounding is, how RAG relates to it, how grounding reduces AI hallucinations, and how to ground LLMs and agents in verified real-world event data - in your environment or on demand via MCP.
- [MCP server](https://docs.predicthq.com/ai/mcp.md): Connect AI agents to verified real-world event context on demand. External grounding for LLMs via the Model Context Protocol—no data pipeline to maintain.
- [PredictHQ MCP in agentic workflows](https://docs.predicthq.com/ai/predicthq-mcp-in-agentic-workflows.md): Reference architectures for autonomous agents that query the PredictHQ MCP server for verified demand context at decision time.
- [Agent skills](https://docs.predicthq.com/ai/agent-skills.md): Install PredictHQ agent skills to give AI coding assistants best-practice integration knowledge - recommended workflow, API selection, and Beam guidance, applied automatically while you build.
- [Bolt](https://docs.predicthq.com/ai/bolt.md): An AI-native notebook inside the PredictHQ WebApp. Bolt answers from live PredictHQ API data—a working example of a grounded agent.


---

# 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://docs.predicthq.com/ai.md?ask=<question>&goal=<endgoal>
```

`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.