Prompt Anatomy Glossary
Shared definitions for core Prompt Anatomy terms, from MCP and RAG tiers to context rot, CLEAR, and prompt registry operations.
Framework
Original frameworks for predictable AI outcomes.
Recommended reading order
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The Model Is Not the System
Teams fail when chat is the product. This framework maps the system around the model—workflow, context, evaluation,...
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Prompt Anatomy Foundations
Implementation stack index—six layers from outcome to governance, with role-based paths into diagnostics, agents,...
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What Is Context Architecture?
How teams decide what models see, when, and why—with a context spec walkthrough, prompt assembly order, context rot,...
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Grounding AI Outputs
A practical framework for grounding AI outputs by combining context architecture, retrieval policy, and evaluation...
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Context Rot in AI Agents
Why agent performance often drops with larger context windows, and how to prevent context rot with architecture and...
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RAG in Production
Implement RAG with governance — choose basic, smart, or agentic patterns and wire eval gates before production traffic.
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Memory Types for AI Systems
Session, episodic, and organizational memory for AI workflows—and when each belongs in your context architecture.
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Evaluation Hooks for AI Workflows
Sample eval cases and pass/fail gates—with YAML example for support-reply-v3.
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Evaluating Agents with CLEAR
A practical framework for evaluating production agents across cost, latency, efficacy, assurance, and reliability...
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The AI Implementation Maturity Ladder
Five levels from ad hoc chat to governed operations—with self-check questions and 90-day moves per stage.
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Prompt Anatomy Glossary
Shared definitions for core Prompt Anatomy terms, from MCP and RAG tiers to context rot, CLEAR, and prompt...
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?
How teams decide what models see, when, and why—with a context spec walkthrough, prompt assembly order, context rot, and data classification.
Prompt Anatomy Foundations
Implementation stack index—six layers from outcome to governance, with role-based paths into diagnostics, agents, eval, and procurement playbooks.
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.