Knowledge Base
High-signal doctrine for production AI architecture and operational discipline. If you're new here, start with the framework path below.
Subject Filter // architecture
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 Heavy Thought Model for AI Systems
Retrieval Boundaries: What Your AI System Is Allowed to Know
Fundamentals of AI
0 ArticlesTechnical Deep Dives
5 ArticlesNLP and Large Language Models (LLMs)
Generative 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
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.
Technical Guide
Generative AI Reference Architecture: A Systems Guide for Production Engineers
An engineer-first generative AI reference architecture: what models optimize, how inference behaves, and which deterministic boundaries make production systems operable.
Practical Implementation Guides
2 ArticlesAI Application in Technical Architecture and Systems Design
Technical Guide
Two-Key Writes: Preventing Accidental Autonomy in AI Systems
A write-gating doctrine: require two independent approvals before any model-proposed action can change external state.
Technical Guide
Architecture Principles for AI Products
Core principles for building maintainable, testable, and resilient AI products.