I’m writing a series on what it actually takes to use AI well in Java development. Not the hype version. The engineering version.

This series covers the full arc: how AI is changing the economics of software work, how it reshapes workflows and prompting, how to design agents and evaluate them properly, and how to build systems that remain reliable and governable. It ends with a question most AI content avoids: where you should deliberately not use it.

One article per week. Here’s the full map.

Foundations

  1. Code Is Cheap. Trust Is Expensive.
  2. Before You Ask AI to Code, Write a Better Spec.
  3. AI Output Gets Better When Your Workflow Gets Stricter.
  4. Prompting Is Not Talking. It’s Interface Design.
  5. There Is No Best AI Model, Only Better Workflow Choices.

Safety and Review

  1. The Biggest Risk With AI Code Is Not Bad Code. It’s Unquestioned Code.
  2. Good Agent Use Starts With Smaller Tasks, Not Smarter Prompts.
  3. Self-Correction Only Works When the System Knows When to Stop.
  4. Agent Orchestration Is Really Workflow Design.

Evaluation

  1. Green Checks Do Not Mean AI Code Is Safe.
  2. AI Systems Are Not Untestable. Your Test Strategy Is Just Too Narrow.
  3. If You Can’t Measure It, You Don’t Know If Your AI System Improved.

Building AI Systems

  1. Good AI Architecture Starts Before You Touch the Model.
  2. AI Reliability Is Mostly About What Happens When the Model Fails.
  3. If You Can’t See Your AI Workflow, You Can’t Debug It.
  4. Governance Is What Prevents AI Workflows From Becoming Expensive Chaos.
  5. Local Models Aren’t Worse. They’re Better for Different Jobs.

Closing

  1. The Most Important AI Skill Is Knowing Where Not to Use It.

This series is for experienced Java developers who are already using AI tools and want to get better without losing engineering discipline.

Each article includes a concrete example, a connection to the Java ecosystem, and a hands-on exercise you can try in your own project.