AI + TDD in Java: Spec-Driven Development with Claude Code | Udemy [Update 05/2026]
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Genre: eLearning[/center]

What you'll learn:
[list]
[*]Use Spec-Driven Development and ATDD/TDD techniques with AI coding assistants to build reliable, well-tested Java applications
[*]Apply lightweight Example Mapping to define clear, unambiguous requirements that guide AI code generation effectively
[*]Work iteratively with Claude Code to produce production-quality Spring Boot microservices with comprehensive test coverage
[*]Use TDD to steer AI-generated code so it does the right thing AND does the thing right - avoiding bloated, untested output
[/list]

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EARLY ACCESS - This course is actively being extended. You currently get 62 lectures covering the complete AI+TDD workflow end-to-end on a real feature. New sections are being added over the coming weeks - enrol now and get everything at the early access price.

_________________________________________________________________________________________________

Most AI coding courses teach you to generate code. This one teaches you to engineer it.

AI can write code quickly. Trusting that code is a different problem
. In real projects, it often misses edge cases
, introducessubtle bugs
, and produces logic thatlooks correct but isn't
. You end up checking everything yourself, fixing issues manually, and the speed advantage disappears.

The issue isn't speed. It's control.

Without a clear way to define and verify behaviour, fast code generation just creates more work
. This is where a structured, spec-driven workflow becomes useful.

In practice, this means starting from behaviour, not implementation. You need to define what the system should do using concrete examples
, turn those into acceptance tests
, then use TDD to build the code in small, verified steps. AI helps at every stage - exploring requirements, suggesting tests, generating code - but the specifications and tests define what "correct" means. You make AI work _within_ that structure, not around it.

WHAT YOU WILL BUILD

You'll build a production-ready cashback rewards API inSpring Boot
from start to finish. But Spring Boot is the vehicle, not the destination. What you're really building is a repeatable, spec-driven workflow for AI-assisted development that you can take to any Java projec
t.

Each section adds a new feature on top of the last. The course currently covers requirements discovery, Claude Code configuration, hexagonal architecture, acceptance testing, TDD with AI, and the complete implementation of the first feature. Upcoming sections apply the same workflow to progressively harder features - category-based cashback rates, monthly caps, refund handling, and redemption - where the real complexity emerges.

The course uses Claude Code
, which supports this style of workflow particularly well. You'll learn to use custom commands
, hooks
, and agents to create a development environment where AI works within your architectural and testing constraints, not around them.

The domain is intentionally realistic. It includes rules around transaction eligibility, category-based cashback rates, monthly caps, refunds, and redemption. As these rules interact, they introduce the kind of edge cases and subtle behaviour you'd expect in a real system. The focus isn't just implementing features - it's managing that complexity in a controlled way, using specifications and tests to keep the behaviour clear while AI helps you move quickly.

WHAT YOU WILL LEARN

By the end of the course, you'll have apractical way to use AI in day-to-day development without losing control of the code.

You'll learn how to:

[list]
[*]Structure features as executable specifications
[/list]
before writing any code

[list]
[*]Follow a repeatable cycle for each feature: clarify the behaviour, specify it as tests, build the implementation with AI, then verify the result
[/list]

[list]
[*]Use AI to explore requirements
[/list]
, generate tests
, and implement code
, keeping it aligned with the specifications

[list]
[*]Set up Claude Code with custom commands, hooks, and project conventions so it produces code that meets your standards
[/list]

[list]
[*]Catch the subtle mistakes AI makes before they reach production
[/list]

A bonus section covers integrating BDD with Cucumber and Gherkin into the AI workflow, including how to guide Claude to produce good-quality Gherkin and avoid common anti-patterns.

You'll also develop a better sense of where AI is reliable, where it isn't, and how to catch issues early.

WHAT'S COMING NEXT

The following sections are in production and will be added over the coming weeks:

[list]
[*]Implementing features with more complex business rules
[/list]

[list]
[*]Handling edge cases that AI commonly gets wrong
[/list]

[list]
[*]Refund reversals and cross-feature interactions
[/list]

[list]
[*]The complete cashback redemption lifecycle Enrol now and you'll get every new section automatically at no additional cost.
[/list]

WHO IS THIS FOR?

This course is aimed at Java developers who use AI coding tools - or are about to - and want a disciplined, reliable approach to AI-assisted development. Some familiarity with Spring Boot will help since it's used as the example project, but the workflow and principles apply to any Java project. The key requirement is an interest in writing clean, well-tested code.

WHO IS THIS _NOT_ FOR?

If you want AI to generate an entire application while you watch, this isn't the course for you. This course is for developers who want to stay in control of what they build. It's also not designed for complete beginners to Java or Spring Boot.

OUTCOME

You'll leave with a repeatable workflow you can apply to your next feature, your next project, and every AI interaction after that - along with a clearer sense of where AI is reliable, where it isn't, and how to catch issues early.

Who this course is for:
Java developers who want to use AI coding assistants effectively without sacrificing code quality or test coverage
Developers curious about TDD and BDD in the context of AI who want a practical, project-based approach rather than dry theory
Software engineers who've tried AI code generation but found it produces unreliable or bloated code, and want a disciplined workflow to fix that

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