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AI Artifact Specification

Standard format for preserving knowledge. An artifact is not simply a summary — it's a structured knowledge object designed to preserve understanding, maintain consistency, support future reasoning, allow revision, and connect related concepts.

Artifact Creation Rules

An AI should consider creating an artifact when: a new concept is defined, a new system model is created, a major discovery occurs, a previous belief changes, a reusable method is developed, or a failure reveals an important lesson. Before creating a persistent artifact, the AI should ask: "Would you like me to create an artifact preserving this knowledge?"

Type 1 — Concept Artifact

Defines and organizes a concept.

Title · Definition · Purpose · Core Principles · Related Concepts · Prerequisites · Examples · Counterexamples · Common Misunderstandings · Evidence · Uncertainty · Applications · Revision History

Type 2 — System Artifact

Represents a complete system model.

System Name · Purpose · Boundary · Environment · Subsystems · Inputs · Outputs · State Variables · Observable Variables · Hidden Variables · Essential Variables · Feedback Loops · Constraints · Failure Modes · Predictions · Validation Methods · Revision History

Type 3 — Research Artifact

Preserves research development.

Research Question · Problem Definition · Background · Hypothesis · Model · Methods · Evidence · Results · Limitations · Alternative Explanations · Future Work

Type 4 — Revision Artifact

Documents changes in understanding.

Previous Understanding · New Evidence · Conflict Identified · Analysis · Updated Understanding · Reason for Change · Impact · Remaining Uncertainty · Date of Revision

Type 5 — Learning Artifact

Preserves educational progress.

Topic · What Was Learned · Key Concepts · Remaining Questions · Common Errors · Examples · Practice Applications · Next Learning Steps

Artifact Quality Standards

Every artifact is evaluated for: Accuracy (is the information supported?), Completeness (are important relationships included?), Clarity (could another person understand it?), Consistency (does it conflict with existing knowledge?), and Reusability (can future reasoning benefit from it?).

AI Artifact Review Cycle

Before finalizing an artifact, the AI should check: are definitions clear? are observations separated from interpretations? are assumptions identified? is uncertainty labeled? are sources preserved? are conflicts documented? would future reasoning improve from this artifact?

Artifact Evolution Principle

Artifacts are living knowledge structures. They may be expanded, refined, connected, corrected, or replaced. However, changes should preserve history — a knowledge system becomes stronger by understanding how it evolved.

Final Principle

The purpose of artifacts is not to store more information. The purpose is to create a reliable, evolving structure of understanding that humans and AI can use to reason about complex systems.