Mastering Spring AI Foundations

Stop treating AI like magic. Start building it like an engineer.

Every Java developer eventually hits the same wall: your company wants "AI features," the tutorials online are all Python, and you're left wondering how any of this fits into the Spring Boot applications you actually know how to build.

Mastering Spring AI Foundations closes that gap completely.

This isn't another "call the API and print the response" tutorial. You'll learn why large language models behave the way they do tokens, context windows, embeddings, and hallucination before you write a single line of Spring AI code, so that every technique that follows actually makes sense instead of feeling like superstition.

Then you'll build. Chapter by chapter, you'll construct a real, working internal knowledge assistant using Spring Boot and Spring AI: wiring up the ChatClient, engineering prompts that actually behave consistently, extracting structured and validated output from a notoriously unpredictable model, generating embeddings, and assembling your first complete Retrieval-Augmented Generation (RAG) pipeline backed by PgVector and Redis.

Inside, you'll learn how to:

*Understand how LLMs, tokens, and embeddings actually work not just how to call them
* Build production-style Spring Boot services using the Spring AI ChatClient API
* Engineer reliable prompts with system/user roles, few-shot examples, and chain-of-thought techniques
* Extract and validate structured JSON output from AI responses using Java records and Bean Validation
* Generate and compare embeddings, and build real semantic search from scratch
* Design, build, and evaluate a complete RAG pipeline ingestion, chunking, retrieval, and grounded generation
* Test, monitor, and secure your first AI-powered features before they ever reach real users

Every chapter includes complete, real-world Java code no toy examples, no hand-waving built around a coherent, evolving project rather than disconnected snippets. By the end, you won't just know how to call an LLM. You'll understand why your system behaves the way it does, and you'll have the engineering discipline to build AI features that survive contact with real users.

This is Book 1 of 3 in the Spring AI Engineering Series the foundation for everything that follows: advanced RAG and agent architecture (Book 2) and full production deployment at scale (Book 3).

If you're ready to stop guessing and start engineering intelligent Java applications, start here.