AI-Ready Docs

Use Our Docs with AI

Feed the Ask Sage documentation to LLMs and AI tools — cleanly and completely

Table of Contents

1. How it works
2. The llms.txt sitemap
3. Full-text documentation
4. Add our docs to your AI tool

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How it works

Built for machines, not just browsers

Large language models read plain text and Markdown far more reliably than rendered HTML — there is much less noise to parse. We publish two machine-friendly views of this documentation so you can give an AI assistant accurate, up-to-date context about Ask Sage.

llms.txt

A structured index of every page — point your AI tool here to browse and fetch what it needs

→

llms-full.txt

The entire documentation set as one plain-text file — load it all into a model's context at once

→

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The llms.txt sitemap

An LLM-friendly index

llms.txt is an emerging standard — an LLM-friendly index of the whole documentation site. It lists every page, grouped by section, with a title and link. Point your AI tool at it when you want the model to know what documentation exists and fetch only the pages it needs.

Add this URL to your AI tool, or paste its contents into a chat:

http://docs.asksage.ai/llms.txt

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Full-text documentation

The whole site in one file

llms-full.txt is the entire documentation set concatenated into a single plain-text file, with navigation, styling, and other HTML noise stripped out. Use it when you want to load the complete documentation into a model's context in one shot.

Note: This file contains the full text of every page and can be large. For most tasks, the llms.txt sitemap plus a few targeted pages works better.

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Add our docs to your AI tool

Three steps to grounded answers

1. Copy the llms.txt URL above.
2. In your AI assistant, add it as a source — or paste its contents into the conversation.
3. Ask your question. The model can now reference Ask Sage documentation directly.

Pro Tip: Start with the llms.txt sitemap and let the model pull individual pages on demand — reach for llms-full.txt only when you need the complete corpus in context.

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