> For the complete documentation index, see [llms.txt](https://docs.tabnine.com/main/llms.txt). Markdown versions of documentation pages are available by appending `.md` to page URLs; this page is available as [Markdown](https://docs.tabnine.com/main/getting-started/tabnine-chat/chat-context/context-context-window.md).

# Understanding Context

Tabnine AI code assistant: Understanding context

Each interaction with Tabnine Chat is essentially a question or an instruction to perform a code-related task.

The basic cycle of the chat sequence is a chain of questions and answers:

* The user asks a question.
* Tabnine returns an answer.

However, the question (or prompt) that you ask is just one part of the equation. There's also the context — the extra layer of information that accompanies your question. This context is provided to the Tabnine model alongside your explicit question, enabling it to deliver more accurate and relevant responses.

Understanding the context can help you get the most out of Tabnine.

### Context used by the Tabnine Chat

The context (or context window) includes the following elements:

1. User questions or instructions
2. Selected block of code from the current file (or the cursor location)
3. Current open file
4. Chat history from the current conversation, including all the previous questions and answers (Note: Chat history isn't shared between different conversations)
5. Relevant files or code blocks from the current user workspace (if Tabnine finds such relevant files)

### Context size limit and priorities

The size of the context window is limited. Tabnine applies all kinds of algorithms and heuristics to pick and prioritize the most relevant parts of the context for each situation.

However, the most important part of the context, besides the question, is the selected code and the open file, especially the areas right above the cursor or the selected block.


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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.tabnine.com/main/getting-started/tabnine-chat/chat-context/context-context-window.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.