# 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: 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: ``` GET https://alaya-ai.gitbook.io/alaya-ai/core-features/data-auto-labelling-toolset.md?ask= ``` The question should be specific, self-contained, and written in natural language. 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.