Measure your readiness to effectively use generative AI tools in your software development workflows ( Java version )
Are you truly ready to code with Generative AI — or are there hidden gaps in your fundamentals?
What you’ll learn
- Assess your readiness to responsibly integrate generative AI software development tools into coding workflows..
- Detect flaws, hidden bugs, and inefficiencies in AI-generated code..
- Evaluate your Java OOP, debugging, and algorithmic reasoning skills..
- Complete a capstone loop: tests-first → prompt engineering→ evaluate → fix..
Course Content
- Welcome & Setup –> 1 lecture • 3min.
- Module 1: Assessment –> 1 lecture • 1min.
- Module 2: Prompt Engineering and JUnit Primer –> 5 lectures • 49min.
- Module 3: AI Critique and Repair –> 1 lecture • 1min.
- Module 4: Prompt Revision Micro Lab –> 1 lecture • 3min.
- Module 5: Capstone Micro-Lab –> 0.
- Solutions and Readiness Check –> 9 lectures • 17min.
Requirements
Are you truly ready to code with Generative AI — or are there hidden gaps in your fundamentals?
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
Who this course is (and isn’t) for
-Java developers who want to use GenAI professionally and safely
-Students preparing for internships, projects, or production code
-Engineers who want to know when not to trust AI output
Isn’t for:
-Absolute beginners to Java
-Learners looking for “copy-paste AI shortcuts”
-A traditional lecture-heavy course
Course format
- Self-paced, hands-on diagnostic workshop
- Approximately 7–10 hours total
- Emphasis on thinking, testing, and verification, not memorization
Make the leap from experimenting with AI copilots to using them responsibly, critically, and effectively