Java Developer to AI Engineer
Call Your First LLM from Spring Boot
Build a small /ask endpoint with Spring AI, run it on a local model, log token use and handle failures. Part 3 of the series.
Learn AI. Build with AI.
Engineering articles on LLMs, RAG, agents, MCP and governance, with a path for Java developers. Written by a backend engineer who builds these systems.
Start here
Java Developer to AI Engineer
A stage-by-stage plan for working Java developers who want to build AI features with Spring Boot. Each part ends with something you build, and every part links back to the roadmap.
3 of 10 parts published. The other 7 are planned and shown in grey.
Read Part 1: the roadmap- Roadmap
- Stage 1
- Stage 1 to 2
- Stage 2
- Structured output and streaming
- RAG with Spring AI and pgvector
- Stage 3
- Agents: the plan, act, observe loop
- MCP servers and clients in Java
- Stage 4
- Evaluating and tracing LLM apps
- Guardrails, approvals and cost control
- Finish
- Your portfolio and interviews
More series
Autonomous Systems Architecture
End-to-end architecture patterns for goal-seeking agents, multi-model dispatch loops, hierarchical retrieval, and runtime governance.
4 parts, intermediate
Claude Code for Java Developers
Install Claude Code and use it on a real Spring Boot project: safely, configured, automated and under control. Every part is one practical task with the exact prompts, commands and results.
1 of 14 parts published, beginner to advanced
Planned: Java Agent Engineering; LLM Foundations & Core Primitives; Prompt Engineering & Evaluation Systems. All series