Knowledge Base
High-signal doctrine for production AI architecture and operational discipline. If you're new here, start with the framework path below.
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Framework Reading Path
The shortest route into the doctrine system: model, containment, regression discipline, then release authority.
Probabilistic Core / Deterministic Shell: Containing Uncertainty Without Shipping Chaos
Two-Key Writes: Preventing Accidental Autonomy in AI Systems
The Minimum Useful Trace: An Observability Contract for Production AI
Error Taxonomy: Classifying AI System Failures Before They Become Incidents
Fundamentals of AI
0 ArticlesTechnical Deep Dives
4 ArticlesGenerative AI
Technical Guide
Probabilistic Core / Deterministic Shell: Containing Uncertainty Without Shipping Chaos
A production architecture pattern: treat the model as a probabilistic component and wrap it in deterministic contracts, budgets, and enforcement so the system stays operable.
Technical Guide
Error Taxonomy: Classifying AI System Failures Before They Become Incidents
A failure-language doctrine for production AI: classify failures by boundary crossed, control missed, and detection path so incidents stop dissolving into vague model blame.
Technical Guide
The Heavy Thought Model for AI Systems
A governed control-plane doctrine for reliable AI architecture: six layers, three disciplines, and one coherent model for turning probabilistic capability into operable systems.
Technical Guide
Retrieval Boundaries: What Your AI System Is Allowed to Know
Retrieval is not a search feature. It is the runtime memory boundary that determines what evidence your AI system is allowed to admit, cite, and act on.