Growth Path

This site uses two mutually orthogonal routes to organize the same set of content:

Growth Path answers “With my current ability, what should I learn next?” and is suitable for most learners.
Capability Map answers “What are the key issues in the whole process of AI Coding”, suitable for architecture design, platform construction and team governance.

Only one copy of the article is maintained, and the stage and professional focus are marked at the same time, so it can be entered from two routes.

Four-stage capability model

Stage	Suitable for the crowd	Learning objectives	Learning key points
Novice stage	People who are new to AI Coding, or have installed tools but don’t know how to use them effectively	Complete simple pages, tool functions or small modules independently	Tool selection, engineering basics, goal description, feedback iteration
Intermediate stage	People who can complete simple tasks and want to improve stability	Stably produce high-quality code and reduce rework and trial and error	Project rules, requirement specifications, execution boundaries, verification projects
Expert stage	People who often handle complex projects or cross-position collaboration	Efficiently promote complex projects and become the backbone of team AI collaboration	Contextual engineering, capability encapsulation, plug-in, multi-Agent collaboration
Master stage	Technical leader, team leader and AI Coding facilitator	Let AI Coding be scaled up and implemented sustainably in the team	Model, gateway, cost, evaluation and team governance

Complete an end-to-end practice from requirements to operational results.
Establish project rules, requirement specifications and verification closed loop.
Precipitate context and expert experience into reusable capabilities.
Finally enter the model, cost, gateway and organizational level governance.

Please see Content Overview for specific topic selection, sequence and acceptance criteria.