Context Engineering vs Context Architecture for Regulated Teams
When a source looks wrong, check what the model actually saw. When rules clash or nobody owns them, name the spec before you retune the pack.
Framework
Original frameworks for predictable AI outcomes.
Recommended reading order
-
The Model Is Not the System
Teams fail when chat is the product. This framework maps the system around the model—workflow, context, evaluation,...
-
Prompt Anatomy Foundations
Prompt Anatomy foundations—the six-layer implementation stack from outcome to governance, with role-based paths into...
-
What Is Context Architecture?
Context architecture is how teams decide what models see, when, and why—layers, a filled context spec, assembly...
-
Context Engineering vs Context Architecture for Regulated Teams
When a source looks wrong, check what the model actually saw. When rules clash or nobody owns them, name the spec...
-
Grounding AI Outputs
A practical framework for grounding AI outputs by combining context architecture, retrieval policy, and evaluation...
-
Context Rot in AI Agents
Why agent performance often drops with larger context windows, and how to prevent context rot with architecture and...
-
RAG in Production
Implement RAG with governance — choose basic, smart, or agentic patterns and wire eval gates before production traffic.
-
Memory Types for AI Systems
Session, episodic, and organizational memory for AI workflows—and when each belongs in your context architecture.
-
Evaluation Hooks for AI Workflows
Sample eval cases and pass/fail gates—with YAML example for support-reply-v3.
-
Evaluating Agents with CLEAR
A practical framework for evaluating production agents across cost, latency, efficacy, assurance, and reliability...
-
The AI Implementation Maturity Ladder
Five levels from ad hoc chat to governed operations—with self-check questions and 90-day moves per stage.
-
Prompt Anatomy Glossary
Shared definitions for core Prompt Anatomy terms, from MCP and RAG tiers to context rot, CLEAR, and prompt...
Prompt Anatomy Glossary
Shared definitions for core Prompt Anatomy terms, from MCP and RAG tiers to context rot, CLEAR, and prompt registry operations.
Evaluating Agents with CLEAR
A practical framework for evaluating production agents across cost, latency, efficacy, assurance, and reliability with thresholds and weekly operating rituals.
RAG in Production
Implement RAG with governance — choose basic, smart, or agentic patterns and wire eval gates before production traffic.
Context Rot in AI Agents
Why agent performance often drops with larger context windows, and how to prevent context rot with architecture and retrieval discipline.
Grounding AI Outputs
A practical framework for grounding AI outputs by combining context architecture, retrieval policy, and evaluation checks in one production system.
The AI Implementation Maturity Ladder
Five levels from ad hoc chat to governed operations—with self-check questions and 90-day moves per stage.
Evaluation Hooks for AI Workflows
Sample eval cases and pass/fail gates—with YAML example for support-reply-v3.
Memory Types for AI Systems
Session, episodic, and organizational memory for AI workflows—and when each belongs in your context architecture.
What Is Context Architecture?
Context architecture is how teams decide what models see, when, and why—layers, a filled context spec, assembly order, and why bigger windows are not a strategy.
Prompt Anatomy Foundations
Prompt Anatomy foundations—the six-layer implementation stack from outcome to governance, with role-based paths into diagnostics, agents, eval, and procurement.
The Model Is Not the System
Teams fail when chat is the product. This framework maps the system around the model—workflow, context, evaluation, and governance.