AI-Assisted Development with Spring Boot
Twenty-nine chapters on building real Spring Boot applications with AI tools as a genuine part of the workflow. Covers GitHub Copilot, Claude Code, MCP servers, plan mode, agent mode, and custom slash commands. Goes past the demos to cover prompt patterns that hold up, spotting AI-generated bugs, and knowing when not to trust the agent.
Coming Soon
What you'll learn
Inline completions with GitHub Copilot: when to accept, reject, and steer
The prompt loop with Claude Code: iterating from rough idea to reviewable code
Prompt patterns that produce working output versus ones that produce slop
Spotting AI-generated bugs, weak tests, and over-abstracted code before code review
CLAUDE.md and persistent project context: making AI tools aware of your conventions
MCP servers: connecting Claude to your database, logs, and running services
Plan mode and agent mode for designing before coding and letting Claude drive
Multi-file refactors, hooks, subagents, and custom slash commands
Debugging production issues, reviewing PRs, and onboarding to unfamiliar codebases with AI
Cost, model selection, and when not to trust the agent
Table of Contents
Getting Started
- 01 Why AI-Assisted Development?
- 02 Your AI Workbench: Copilot + Claude Code + CinéTrack
- 03 Your First Prompts: From Question to Working Code
- 04 Copilot in the IDE: Inline Completion as a Skill
- 05 The Prompt Loop: Iterating with Claude Code
- 06 Prompting Patterns That Hold Up
Code Quality
- 07 Reading AI Code Like It's Someone Else's PR
- 08 Common AI Slop and How to Spot It
- 09 Tests as Your Safety Net for AI-Generated Code
- 10 Why AI Tools Fail in Real Codebases
Context and Configuration
- 11 Feeding the AI: Files, Symbols, and Conventions
- 12 CLAUDE.md and Persistent Project Context
- 13 MCP: Connecting AI to Your Database, Logs, and APIs
- 14 Plan Mode: Designing Before Doing
Advanced Workflows
- 15 Agent Mode: Letting Claude Code Drive
- 16 Multi-File Refactors and Cross-Cutting Features
- 17 Hooks, Subagents, and Custom Workflows
- 18 Custom Slash Commands and Skills
Real-World Tasks
- 19 Debugging with AI: Stack Traces, Heisenbugs, Performance
- 20 Code Archaeology: Onboarding to a Strange Codebase
- 21 Migrations with AI: Library Upgrades, Java Bumps, Rewrites
- 22 AI for SQL and Data: Query Design, Index Triage, Slow Hunts
- 23 Reviewing Other People's PRs with AI
- 24 Documentation, ADRs, and Diagrams with AI
- 25 Security, Secrets, and What NEVER to Send to AI
Strategy
- 26 Cost, Latency, and Model Selection
- 27 AI in CI/CD: Agents in Pull Request Pipelines
- 28 When to Trust the Agent and When Not To
- 29 Capstone: Ship a Feature End-to-End with Agents