A glossary corpus must be designed for clarity, scalability, and integration. Terms need clear naming conventions and definitions must vary in depth. A system for adding new terms and updating existing ones should be well-defined.
By Pawan Nayar · from the LLOS AI Learning record
A glossary corpus must be designed for clarity, scalability, and integration.
Terms need clear naming conventions and definitions must vary in depth. A system for adding new terms and updating existing ones should be well-defined.
Each term in the glossary needs a unique name. This name combines a domain and the term itself, like 'physics/inertia'. This prevents confusion when words have many meanings. Common words with multiple meanings get separate entries for each meaning. Definitions can be short, standard, or deep. The default depth is set for a group of terms. A specific term can override this default. This allows flexibility in how much detail is provided. New terms are added to a review queue. The main system continues to work without waiting for glossary updates. A human user approves new terms or changes. This keeps the glossary accurate and up-to-date. An AI model can create new definitions for specific traditions. This happens when enough related information is available. A human can always edit these AI-generated definitions. The glossary uses different sources for terms. These sources have a priority order. This ensures the most reliable information is used first. The system tracks where each definition comes from. For future growth, the glossary will start with JSON files. It will switch to a database when it gets very large. Each term will have a unique web address. A separate page will help users find terms with multiple meanings.
A developer is building an AI model that explains religious festivals. The developer needs a glossary of terms. The developer decides that 'festival/prasad' will be a term. The definition for 'prasad' will be 'standard' depth. If a new term like 'langar' appears in a blog, it goes into a review queue. The AI model continues to generate content while 'langar' waits for human approval. Once approved, 'sikh/langar' is added to the glossary.
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.
An AI system can organize its knowledge into three levels. Level 1 holds basic information like names or words.
A glossary needs a plan for naming terms. This plan includes grouping terms by their subject area.
A keyword must appear in at least five long content panels. These panels are usually part of different sections.
The AI model should generate stories in both languages at the same time. A shared plan, like a JSON brief, guides both generations.
A glossary corpus must be designed for clarity, scalability, and integration. Terms need clear naming conventions and definitions must vary in depth. A system for adding new terms and updating existing ones should be well-defined.