The Context Mastery Guide

Stop hit-and-miss prompting.
Decompose like a pro.

The #1 reason AI fails isn't the model—it's the scope. Learn how to turn vague feature ideas into a perfect sequence of AI-ready tasks.

Start Mastering Scoping
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The Difference is Senior Judgment

The Junior Approach

  • ✗ "Build me a complete SaaS dashboard with auth."
  • ✗ Providing 50 files of context for a 10-line fix.
  • ✗ Hitting 'Regenerate' when the output is too long.

The Senior Approach

  • ✓ "Decompose this feature into 5 stateless modules."
  • ✓ Isolating context to only relevant types and helpers.
  • ✓ Steer with atomic changes, never full rewrites.

Learn the Atomic Scoping Framework

Chapters from the course that turn you into a scoping master.

02

Scoping Work for AI

Learn task decomposition. Turn feature ideas into AI-sized chunks that actually ship without hallucinations.

03

Context Control

Master the 'Only What You Need' principle. Why dumping your whole repo is the fastest way to get bad code.

04

Prompt Phases

The Discovery phase. How to ask AI to help you scope before you write a single line of implementation code.

Scoping FAQ

Ideally, an AI task should result in 20-50 lines of code change. If the AI has to write 200 lines at once, the probability of a logic error increases by 80%.

Large context windows help the AI 'see' your repo, but 'attention' is a finite resource. Even 2M context models perform better when focused on small, specific files.

Yes. We treat requirements engineering as a core coding skill. If you can't define what you want, no AI model in the world can build it for you.

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Master the skill that makes AI useful for complex production apps.

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