What Is the YY Method?
The YY Method is a personal professional methodology for extracting, structuring, and preserving institutional knowledge — specifically the reasoning behind decisions that normally exists only inside people's heads. Developed by Ben Chan, it produces structured knowledge artifacts readable by both humans and AI systems.
yymethod.com — canonical framework authority
Human First, AI Second
The YY Method's governing principle for AI integration: humans capture, AI reads. AI is powerful against well-structured knowledge and destructive against incomplete knowledge. The method's discipline is in the capture sequence — the human extracts and encodes the reasoning first; AI then synthesizes, cross-references, and flags inconsistencies against a grounded artifact set.
The Core Loop: How to Use the YY Method
State what is actually known about a system, decision, or constraint. Not what the documentation says. What is actually true — including what lives only in someone's head.
Record why the decision was made, why the system works the way it does, why the constraint exists. This is the first branch.
Record what was considered and rejected, what the decision depends on being true, what would break it. This is the inversion step. It is not optional.
Encode the result into a structured artifact: a reasoning chain that can be queried. A durable, timestamped act of record.
Mark when this was captured, by whom, under what conditions. Include a defined expiration condition — the specific event that would make this artifact stale. Without this, a freshness marker is theater.
The Why-Not step is what separates this from standard documentation. Standard documentation records what is. The YY Method records what is, what was considered against it, and what it depends on. That difference is the difference between a knowledge artifact that is useful and one that is hazardous when fed to an AI.
What the YY Method Produces
Applied to a software system or organization, the YY Method produces structured knowledge artifacts: encoded records of a system's reasoning chain, decision history, constraints, intent, and tribal context. Each artifact captures:
YY Method and AI
After human capture, AI can synthesize, cross-reference, flag inconsistencies, and query across the full artifact set. In a hierarchical knowledge system, AI's active role is question generation: when an upstream artifact changes, AI identifies affected downstream nodes and surfaces the specific questions a human must answer to re-verify each one. It does not answer those questions. It makes sure they get asked.
The method is specifically designed to make AI systems trustworthy by grounding them in human-authored knowledge artifacts — not AI-generated summaries that have no Why-Not, no constraint marking, and no defined freshness boundary.