> For the complete documentation index, see [llms.txt](https://alaya-ai.gitbook.io/alaya-ai/llms.txt). Markdown versions of documentation pages are available by appending `.md` to page URLs; this page is available as [Markdown](https://alaya-ai.gitbook.io/alaya-ai/core-features/data-auto-labelling-toolset.md). # Data Auto-Labelling Toolset Data auto-labelling is the next stage of self-supervised AI evolution. Auto-labelling technologies leverage human expertise to maximally benefit from autonomous learning systems, improve AI capabilities and achieve AI alignment objectives. Powered by a proprietary three-layer intelligent optimisation architecture, Alaya AI empowers AI users and developers by providing a set of exclusive data auto-labelling toolsets designed to support a wide range of data types and optimisation technologies. Alaya AI’s proprietary data auto-labelling models utilise RLHF fine-tuning to maximally harness human knowledge and expertise through decentralised data contributor networks and knowledge communities, vastly improving data labelling efficiency while significantly reducing costs. Our system is able to achieve over 80% verification rate for most common AI data categories and is capable of processing both static and dynamic visual data in real time. --- # 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 dynamically by asking a question. Perform an HTTP GET request on the current page URL with the `ask` query parameter, and the optional `goal` query parameter: ``` GET https://alaya-ai.gitbook.io/alaya-ai/core-features/data-auto-labelling-toolset.md?ask=&goal= ``` `ask` is the immediate question: it should be specific, self-contained, and written in natural language. `goal` is optional and describes the broader end goal you are ultimately trying to accomplish on behalf of the user. GitBook uses it to tailor the answer towards what is most useful for that goal. 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.