# AI support

We treat AI agents as first-class readers of our documentation. The same Taiga UI knowledge is published in
a few complementary shapes — a flat context file, a query server, and reusable skills — so whatever your
assistant can consume, it reaches the current, version-correct API instead of guessing from memory.

That matters more than it sounds. An assistant can scaffold a screen in seconds, but it generates components
from training data frozen in time — so the code often compiles yet quietly targets an API that has moved on,
and a growing app drifts out of sync with itself. Point the agent at a live, versioned source and generated
code stays aligned with the components you actually ship.

## Why it helps

- **Current, not remembered**
— the agent reads today's API from a live source instead of recalling a stale one from its training
cut-off.

- **Generated from source**
— the same pipeline that builds these docs and their runnable examples, so what the agent reads is
what you ship.

## Choose a layer

These aren't competing options — they are a stack of roles over one body of knowledge.

Skills Follow a proven Taiga workflow and know what to double-check.

MCP server Ask for one component's exact, current API on demand.

llms.txt Drop the current API into any model's context window.

`llms-full.txt`
is the generated data,
`@taiga-ui/mcp`
is a query interface over that same data, and skills are the method that consumes it. When they
disagree, the live source wins.