An AI system uses different levels of profiles to create relevant content. These levels organize raw data, deep research, and generation rules. This structure helps the AI produce varied and appropriate outputs.
By Pawan Nayar · from the LLOS AI Learning record
An AI system uses different levels of profiles to create relevant content.
These levels organize raw data, deep research, and generation rules. This structure helps the AI produce varied and appropriate outputs.
An AI system can organize its knowledge into three levels. Level 1 holds basic information like names or words. Level 2 contains detailed research on specific topics. Level 3 includes rules for how the AI should create new content. This layered approach ensures the AI has all the necessary information. It also helps the AI generate diverse and accurate outputs.
A developer builds an AI for educational content. Level 1 stores lists of names and exam board details. Level 2 has deep information on grammar topics and law exam patterns. Level 3 contains templates for question stems and rules for difficulty. When the AI creates a practice question, it uses all three levels. This makes the question relevant to a specific student's needs.
Resolved from this lesson's own record — real definitions, not invented for this page.
More from the same record, pulled by shared topic — not written new for this page.
Each term in the glossary needs a unique name. This name combines a domain and the term itself, like 'physics/inertia'.
A developer needs a full specification document. This document describes how to build new content.
A developer can use a script to pick one item from each defined group. Each group is like a 'bucket' of options.
An AI model can build a network of 'Super-Assets'. These are like big applications.
An AI system uses different levels of profiles to create relevant content. These levels organize raw data, deep research, and generation rules. This structure helps the AI produce varied and appropriate outputs.