Generative AI Coding Readiness Check

Measure your readiness to effectively use generative AI tools in your software development workflows ( Java version )

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.

Requirements
● 1 or more years of experience with Java programming — you should be comfortable writing classes, methods, and using core OOP concepts like inheritance and encapsulation.
● Familiarity with basic data structures and algorithms, such as arrays, lists, maps, loops, and sorting.
● JUnit or similar testing experience — basic knowledge of writing and running unit tests.
● A computer with Java 17 installed and a simple IDE or text editor (e.g., IntelliJ, VS Code, or Eclipse).

Description
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

Who this course is for
■ Early-career Java programmers with about 1–2 years of experience who want to know if they’re ready to integrate Generative AI into their software development workflow.
■ Students and recent graduates who have completed an introductory Java OOP course and want to test their skills before relying on AI coding assistants.
■ Self-taught developers who’ve been coding in Java and want a structured way to identify skill gaps before diving into AI-driven software development.
■ Teams or individual developers looking for a skills check-up to ensure strong fundamentals in debugging, algorithmic thinking, and prompt crafting before adopting generative AI tools.

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