Knowledge and Intelligence Framework

Knowledge is the accumulation of facts, while intelligence involves the creation, observation, evaluation, and assessment of these facts. AI systems, particularly LLMs (Large Language Models), demonstrate intelligence in their ability to process and apply knowledge but rely heavily on pre-acquired data for their operations.

A proposed framework integrates LLMs with structured relational databases to enhance knowledge management across specialized fields. This system would use LLMs for initial response generation, expert collaboration to refine outputs, and a tree-based hierarchy for efficient navigation. Responses, actions, and physical items would be organized into a "Body of Knowledge" app, tailored to the specific needs of each field.

A working prototype has been developed, focusing on the field of animals. In this database, the individual animal is the central entity. Key features include:

• A physical item table for inventory (e.g., livestock panels, dog collars, hormone drugs) that can be attached to animals or groups, inventoried, sold, or managed.
• An action table for veterinary procedures, training sessions, competitions, and other activities involving animals or herds. Actions can link to follow-up actions, have unique GUIDs, and are type-specific with scalable customization.
• A tasks system allowing tasks to function independently or link with actions, enabling real-time workflows and references.
• A tree-based navigation structure to explore items, actions, and knowledge containers with parent-child relationships.
• Integration of knowledge base containers to provide contextual data linked to real-time workflows and physical entities.

This foundation is well-suited for expanding functionality, such as integrating LLMs to suggest follow-up actions, generate recommendations, or provide a conversational interface. This system offers scalable and customizable knowledge management solutions across various domains.