AI Search Optimization relies on a small set of core concepts that describe how AI systems reason about solutions under uncertainty.
These concepts are descriptive, not procedural.
They define what must be understood, not how to achieve it.
Entity
An entity is a stable, identifiable concept that an AI system can reason about consistently.
Entities represent “what something is” in a way that allows comparison across contexts.
Entities are evaluated for clarity, boundaries, and internal consistency.
Offer
An offer describes what an entity provides or enables in a specific context.
It defines applicability without implying universality.
Offers exist only within scope.
Outside that scope, an offer is not evaluated.
Scope
Scope defines the conditions under which an entity or offer is valid.
It limits interpretation and reduces ambiguity.
Explicit scope reduces the risk of overgeneralization.
Constraint
Constraints describe when an entity or offer does not apply.
They are not exceptions; they are boundaries.
Constraints are critical for safe recommendation.
Representation
Representation refers to how an entity, offer, and its constraints are internally modeled by an AI system.
Stable representation enables consistent evaluation.
Unstable representation increases risk.
Misrepresentation Risk
Misrepresentation risk is the likelihood that an AI system could recommend a solution inaccurately or without sufficient qualification.
High misrepresentation risk leads to exclusion, not correction.
Recommendation
Recommendation is the act of selecting a solution that can be included in a response with minimal explanatory risk.
Recommendation is a decision, not a ranking.
These concepts are interdependent, but not sequential.
They describe a reasoning space, not a process.