Concept
AI Reasoning
The ability of AI systems to use context, semantics, knowledge, and evidence to form useful conclusions.
Position in the Trusted Data Framework
AI Reasoning
Living Graph View
AI Reasoning in the concept network
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Relationship Matrix
AI Reasoning as a network node
Needs shared meaning to reason across sources.
Uses connected knowledge to improve explanation and retrieval.
Requires controls for risk, accountability, transparency, and human oversight.
Concept Relationships
How AI Reasoning works with other concepts
Needs shared meaning to reason across sources.
Uses connected knowledge to improve explanation and retrieval.
Requires controls for risk, accountability, transparency, and human oversight.
Definition
AI Reasoning combines model capability with trusted knowledge structures so AI can move from retrieval toward defensible inference.
Role in the Trusted Data Framework
Transforms knowledge infrastructure into decision-supporting intelligence.
Practical Examples
- RAG over knowledge graph
- Policy-aware assistant
- Semantic question answering
Prototype Direction
- consulting-navigator
Consulting Questions
- What problem does this concept help diagnose in the client environment?
- Which upstream concepts must be in place before this concept becomes reliable?
- What evidence would prove that this concept is working in practice?