Engineering Series

Structured learning pathways organized into foundational theory and hands-on systems architecture.

Learn AI

Fundamentals: LLMs, prompting, embeddings, retrieval, evaluation

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.
Beginner, 3 of 10 parts published, about 50 min of reading

LLM Foundations & Core Primitives
Core mechanics of large language models: tokenization, attention mechanisms, embeddings, and context window management.
Prompt Engineering & Evaluation Systems
Systematic prompt development, automated benchmark suites, and LLM-as-a-judge evaluation frameworks.

Systems: agents, MCP, orchestration, governance, Java agents

Autonomous Systems Architecture
End-to-end architecture patterns for goal-seeking agents, multi-model dispatch loops, hierarchical retrieval, and runtime governance.
Intermediate, 4 parts, about 30 min of reading
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.
Beginner to advanced, 1 of 14 parts published, about 17 min of reading

Java Agent Engineering
Building high-throughput, memory-efficient agent loops with Java 21 virtual threads, Spring AI, and MCP integrations.