This course is a hands-on readiness self-check for Java programmers with 1 or more years of experience who want to use Generative AI responsibly, not blindly. As AI copilots and “vibe coding” become more common, the real challenge isn’t writing less code — it’s knowing when AI output is wrong, inefficient, insecure, or misleading. This course helps you find out where you stand before you rely on AI in serious projects. Rather than long lectures, this course uses structured diagnostics and micro-labs to expose your strengths, gaps, and blind spots.
What you’ll practice
Through short, focused exercises, you will work on:
- Java fundamentals
Encapsulation, constructors, APIs, control flow, and correctness reasoning - Debugging & logical reasoning
Catching subtle bugs, infinite loops, incorrect conditionals, and edge cases - Algorithmic thinking
Big-O tradeoffs, data structure selection, and performance awareness - AI critique labs
Identifying over-engineered solutions, fake fixes, hidden bugs, and unsafe patterns in AI-generated code - Prompt engineering for developers
Writing structured prompts with roles, goals, constraints, and acceptance tests - Capstone GenAI micro-project
A full AI-assisted workflow: tests-first → prompt design → AI generation → verification → reflection
What you’ll walk away with
By the end of the course, you’ll generate your own GenAI readiness verdict. That is, are you:
- Ready Now — strong fundamentals and disciplined AI usage
- Ready with Guidance — mostly solid, but needs practice with prompts and verification
- Not Yet Ready — focus first on strengthening Java, logic, and testing skills
You’ll also leave with:
- Model solutions and rubrics for every exercise
- A reusable tests-first prompt template for GenAI coding
