Glossary

The academic discipline that studies how humans design, govern, verify, and collaborate with Artificial Intelligence through structured decision-making.

The structured logic architecture — business, project, and technical — that governs how AI understands and executes within defined parameters.

The red lines, stone walls, and stop-loss mechanisms that prevent AI from operating beyond its mandate.

The decision authority structure defining who approves, who directs, and how AI is held accountable.

The multi-layered verification protocols ensuring AI output meets defined quality and accuracy standards before acceptance.

The continuous quality cycle and pyramid that maintain standards throughout the entire AI lifecycle.

The foundational architecture stating that humans create direction, AI accelerates execution, and humans remain responsible.

The project lifecycle from idea to successful implementation, governed by logic and human oversight at every stage.

The practice of optimising communication with AI. Solves communication, not decision-making. A subset of AI Logic Engineering.

The Twenty Principles that form the ethical and operational foundation of the discipline.

The principle that every completed AI initiative feeds back into the framework, improving the methodology with every application.

The explicit, documented designation of who decides, who approves, and who is accountable — defined before AI is engaged.