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

# What is knowledge modelling?

Knowledge modelling in Rainbird is about representing your domain of expertise, including how you make decisions, solve problems and apply your knowledge in a way that can be interpreted by our engine to reason, make decisions, and solve problems in a manner similar to yourself.

Using the Rainbird Studio, you can build models of your knowledge as a Knowledge Map (KM), without having to write any code.

The goal of building a Knowledge Map is to be able to **query** it (i.e. ask it a question) and for the reasoning engine to use the logic and data encoded within it to be able to provide you with a **query result**.

The guides in this section will help you understand the features of the Rainbird Studio necessary to build the structure and logic of a knowledge map and test it by making queries to it.

{% hint style="info" %}
More detailed training is available in the [Rainbird Academy](https://academy.rainbird.ai/), which provides practical examples of how you can use these features.
{% endhint %}


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# 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 by asking a question.

Perform an HTTP GET request on the following URL with the `ask` and `goal` query parameters:

```
GET https://docs.rainbird.ai/rainbird/knowledge-modelling/overview.md?ask=<question>&goal=<user_goal>
```

`ask` is the immediate question: it should be specific, self-contained, and written in natural language.
`goal` is what the user is ultimately trying to achieve, the reason they need the answer. Sharing it helps GitBook give you a better, more relevant answer. A goal is most helpful when it describes the outcome the user wants rather than restating the question. For example, with `ask=how do I create an API token`, a goal like `automate deployments from our CI pipeline` lets GitBook tailor the answer to that use case.

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.