LLM Docs

AIOZ Storage provides machine-readable documentation endpoints for coding assistants, AI agents, and retrieval workflows. Use these endpoints when you want an AI tool to ingest AIOZ Storage context directly instead of navigating the full documentation UI.

This page is intended for developers, agent builders, and teams integrating AIOZ Storage into AI-assisted workflows.

What these endpoints contain

These endpoints provide machine-readable versions of the AIOZ Storage documentation for AI-assisted workflows.

• llms.txt contains a lightweight index of the documentation structure, with links to relevant sections.
• llms-full.txt contains the full concatenated documentation corpus for broader technical context.
• These files are intended for retrieval, summarization, and coding assistance workflows rather than regular docs browsing.

Which one should I use?

• Use llms.txt when you need a lightweight documentation index for discovery, navigation, or limited-context prompts.
• Use llms-full.txt when you need broader implementation context across concepts, SDKs, APIs, and platform behavior.
• As a general rule, start with llms.txt for discovery and fetch llms-full.txt only when the task requires deeper technical context.

Lightweight index for discovery

Use the /llms.txt endpoint to fetch a summarized index of the AIOZ Storage documentation. This is useful for high-level discovery and quick routing to relevant sections.

curl https://aiozstorage.network/docs/llms.txt

Full corpus for deep context

Use the /llms-full.txt endpoint to fetch the complete concatenated AIOZ Storage documentation. This is useful when your AI assistant needs broader implementation context across the full docs set.

curl https://aiozstorage.network/docs/llms-full.txt

Common use cases

1. Feeding an AI coding assistant with AIOZ Storage context: load llms.txt or llms-full.txt into your assistant's context window so it can answer AIOZ Storage questions accurately.
2. Building a lightweight retrieval layer for internal support bots: use llms.txt as an index to route queries, then fetch specific sections as needed.
3. Loading the full docs corpus for deeper implementation help: feed llms-full.txt into a RAG pipeline or long-context model for comprehensive technical assistance.

Operational guidance

Start with llms.txt for discovery. Fetch llms-full.txt only when the task requires broader implementation context.

Important notes

• These endpoints are generated from the current published AIOZ Storage documentation.
• For UI-specific details, edge cases, or recent changes, always verify against the canonical docs pages (opens in a new tab).
• Use llms.txt for lightweight retrieval and llms-full.txt only when broader context is required.
• The published documentation pages remain the source of truth for implementation details.