From code assistant to AI-native development workflow. This path teaches developers how to use AI tools as true development partners. Learn to design, build, and ship features faster, work effectively in both new and existing codebases, and apply AI in a way that scales across teams.
By Andy Larkin · AWS APJ Community Hero of the Year 2024
Skill objectives
Chapter 01
Introduction
Welcome to the course. Get an overview of the learning path and what you will be able to do by the end.
Chapter 02
Why AI Changes Everything for Developers
Understand the real shift: how LLMs work, the exponential capability curve, and two complementary interaction styles.
Chapter 03
The Core Skill: Thinking in Prompts
Master the transferable skill: structuring prompts with task, context, constraints, and examples. Learn the difference between prompt engineering and context engineering.
Chapter 04
Designing Your Product with AI
Use AI as a thinking and design tool before writing code. Brainstorm, generate mockups, iterate visually, and produce specs in minutes instead of days.
Chapter 05
Getting Started with AI Development Tools
Get hands-on with both AI paradigms: Claude Code as an autonomous terminal agent, and Cursor as an AI pair programmer in the IDE.
Chapter 06
Choosing the Right Tool for the Job
Build the judgment layer: when to use Cursor, when to use Claude Code, when to use both, and when to stop using AI entirely.
Chapter 07
Building a Real Feature End-to-End
The flagship module: take a feature from spec to working code. See real mistakes, debugging loops, and judgment calls switching between tools.
Chapter 08
Working with Existing Codebases
The reality check: explore, understand, and safely modify codebases you did not write. Learn why AI struggles with implicit relationships and how to bridge the gap.
Chapter 09
AI in Your Team's Workflow
Shift from individual productivity to team capability. Set up team context files, define safe change classes, measure impact, and avoid common adoption mistakes.
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