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Generative AI for Java Developers: Build Scalable RAG, Agents, and Chatbots using Spring AI and LangChain4j
Generative AI is no longer a Python-only game.
If you build enterprise systems in Java, this book shows you how to design, deploy, and scale production-ready AI applications, without leaving the Spring ecosystem.
What This Book Allows You to DoThis book empowers you to move beyond demos and prototypes and engineer real-world AI systems using Java. You’ll learn how to build intelligent applications that reason, retrieve knowledge, call tools, maintain memory, and operate safely at scale, all with the architectural rigor enterprises demand.
By the end, you’ll be able to design AI-powered Java systems that are observable, secure, testable, and cost-aware, ready for real users and real workloads.
About the TechnologyThis book focuses on Spring AI and LangChain4j, the leading Java frameworks for integrating Large Language Models (LLMs) into modern applications. You’ll work with OpenAI, Azure OpenAI, AWS Bedrock, and local models via Ollama, while leveraging Java 21, Spring Boot, Docker, and Kubernetes.
Instead of treating AI as a black box, the book shows how to architect intelligence using proven enterprise dependency injection, streaming, structured outputs, retrieval-augmented generation (RAG), agents, guardrails, observability, and cloud-native deployment strategies.
Book SummaryGenerative AI for Java Developers is a practical, production-first guide for Java engineers who want to build intelligent systems without sacrificing reliability, scalability, or security. It bridges the gap between cutting-edge AI capabilities and enterprise-grade software engineering, showing how LLMs can be treated like any other infrastructure dependency, predictable, testable, and maintainable.
Across progressive chapters, you’ll learn how to construct scalable RAG pipelines, manage memory and context windows, build autonomous agents, implement safety guardrails, test non-deterministic AI behavior, and monitor cost and performance in production. The book adopts a pragmatic approach, using Spring AI for core infrastructure and LangChain4j where advanced agentic behavior is required, giving you a flexible, future-proof toolkit for Java-based AI development.
What’s Inside This Book?Build production-ready RAG pipelines with vector databases and hybrid search
Design AI agents using tools, function calling, and reasoning loops
Implement memory, context management, and persistence for conversational AI
Enforce guardrails, security, and PII protection in enterprise environments
Test, evaluate, and debug non-deterministic AI systems with confidence
Monitor latency, token usage, and cost using observability best practices
Deploy and scale AI applications with Docker, Kubernetes, and cloud-native patterns
Who This Book Is ForThis book is written
Java developers transitioning into Generative AI and LLM applications
Spring Boot engineers building enterprise AI solutions
Backend developers tired of fragile Python glue code in producti
If you build enterprise systems in Java, this book shows you how to design, deploy, and scale production-ready AI applications, without leaving the Spring ecosystem.
What This Book Allows You to DoThis book empowers you to move beyond demos and prototypes and engineer real-world AI systems using Java. You’ll learn how to build intelligent applications that reason, retrieve knowledge, call tools, maintain memory, and operate safely at scale, all with the architectural rigor enterprises demand.
By the end, you’ll be able to design AI-powered Java systems that are observable, secure, testable, and cost-aware, ready for real users and real workloads.
About the TechnologyThis book focuses on Spring AI and LangChain4j, the leading Java frameworks for integrating Large Language Models (LLMs) into modern applications. You’ll work with OpenAI, Azure OpenAI, AWS Bedrock, and local models via Ollama, while leveraging Java 21, Spring Boot, Docker, and Kubernetes.
Instead of treating AI as a black box, the book shows how to architect intelligence using proven enterprise dependency injection, streaming, structured outputs, retrieval-augmented generation (RAG), agents, guardrails, observability, and cloud-native deployment strategies.
Book SummaryGenerative AI for Java Developers is a practical, production-first guide for Java engineers who want to build intelligent systems without sacrificing reliability, scalability, or security. It bridges the gap between cutting-edge AI capabilities and enterprise-grade software engineering, showing how LLMs can be treated like any other infrastructure dependency, predictable, testable, and maintainable.
Across progressive chapters, you’ll learn how to construct scalable RAG pipelines, manage memory and context windows, build autonomous agents, implement safety guardrails, test non-deterministic AI behavior, and monitor cost and performance in production. The book adopts a pragmatic approach, using Spring AI for core infrastructure and LangChain4j where advanced agentic behavior is required, giving you a flexible, future-proof toolkit for Java-based AI development.
What’s Inside This Book?Build production-ready RAG pipelines with vector databases and hybrid search
Design AI agents using tools, function calling, and reasoning loops
Implement memory, context management, and persistence for conversational AI
Enforce guardrails, security, and PII protection in enterprise environments
Test, evaluate, and debug non-deterministic AI systems with confidence
Monitor latency, token usage, and cost using observability best practices
Deploy and scale AI applications with Docker, Kubernetes, and cloud-native patterns
Who This Book Is ForThis book is written
Java developers transitioning into Generative AI and LLM applications
Spring Boot engineers building enterprise AI solutions
Backend developers tired of fragile Python glue code in producti
306 pages, Kindle Edition
Published January 5, 2026
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