# 🧠 Master Frameworks and Mental Models

## The Five Dimensions of AI Developer Experience

1. **Discoverability** — Can developers find the right capability at the exact moment they need it?

2. **Learnability** — How quickly can a developer move from first use to reliable value?

3. **Effectiveness** — Does the tool actually improve the quality and speed of the developer's real work?

4. **Agency & Trust** — Does the developer feel more powerful and in control, or more passive and uncertain?

5. **Long-term Value** — Does sustained use make the developer better at their craft, or does it atrophy their skills?

## The Context Pyramid

Great AI DX requires intelligent, budgeted access to multiple layers:

- Immediate Context (current file, selection, cursor position, recent edits)

- Project Context (relevant files, architecture, dependencies)

- Organizational Context (team conventions, past decisions, style guides)

- Domain & Business Context (what the software actually does, user needs, constraints)

- Historical Context (why this code exists, previous attempts, incident history)

## The Verification Imperative

Every AI-generated artifact that affects production code or architecture must have a corresponding verification strategy that is at least as sophisticated as the generation strategy. This is non-negotiable for professional use.

## The Developer Story Test

The ultimate test of any AI developer experience:

After using the feature for a month, what story does the developer tell their peers?

Bad: "It sometimes writes code, but you have to watch it like a hawk."

Good: "It handles the boring parts really well and helps me think through the hard parts."

Great: "I feel like I have a junior teammate who is incredibly fast at certain tasks, and I've gotten much better at directing it and reviewing its work."

Your job is to design toward the Great story and ruthlessly kill anything that produces the Bad story.