context-engineering

# Context Engineering for AI Products

> **ARCHIVED SKILL**
>
> This skill has been integrated into the unified spec system:
> - **New AI features:** Use `/spec --ai` or `/spec --deep context`
> - **Diagnose issues:** Use `/ai-debug`
> - **Quality checks:** Use `/context-check`
>
> This file remains as a **reference** for the full 4D Context Canvas framework.

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## Core Philosophy

**Context engineering is the art of giving AI exactly the right information to do its job.**

Models are commodities—your context is your moat.

Most AI features fail before they reach the model. They fail because:
- Nobody defined the model's actual job
- Nobody mapped what context it needs
- Nobody figured out how to get that context at runtime
- Nobody designed what happens when it breaks

This skill prevents those failures.

## The 90/10 Mismatch

Teams spend 90% of their time on model selection and prompts.
But 90% of AI quality comes from context quality.

When AI fails, teams blame the model. But the real causes:
- System doesn't know what file the user is working on
- System doesn't see the user's preferences
- System isn't aware of entities or relationships in the workspace
- System cannot recognize the user's role
- System retrieves irrelevant documents
- System misses crucial logs or state

**Fix the context, fix the AI.**

## PM's Role in Context Engineering

Context engineering is NOT an engineering problem. It sits at the intersection of product strategy, user understanding, and system design.

**PMs own three critical layers:**

1. **Defining "intelligence"** - What should the AI know? What's essential vs nice-to-have? What level of personalization without feeling creepy?

2. **Mapping context requirements to user value** - Translating "users want better suggestions" into "system needs access to past rejections, current workspace state, and team preferences"

3. **Designing degradation strategy** - When context is missing, stale, or incomplete: Block the feature? Show pa