Publisher's Synopsis
This is the first hands-on guide that takes you from a simple "Hello, LLM" to production-ready microservices, all within the JVM. You'll integrate hosted models such as OpenAI's GPT-4o, run alternatives with Ollama or Jlama, and embed them in Spring Boot or Quarkus apps for cloud or on-pre deployment.
You'll learn how prompt-engineering patterns, Retrieval-Augmented Generation (RAG), vector stores such as Pinecone and Milvus, and agentic workflows come together to solve real business problems. Robust test suites, CI/CD pipelines, and security guardrails ensure your AI features reach production safely, while detailed observability playbooks help you catch hallucinations before your users do. You'll also explore DJL, the future of machine learning in Java.
This book delivers runnable examples, clean architectural diagrams, and a GitHub repo you can clone on day one. Whether you're modernizing a legacy platform or launching a green-field service, you'll have a roadmap for adding state-of-the-art generative AI without abandoning the language-and ecosystem-you rely on.
What You Will Learn
- Establish generative AI and LLM foundations
- Integrate hosted or local models using Spring Boot, Quarkus, LangChain4j, Spring AI, OpenAI, Ollama, and Jlama
- Craft effective prompts and implement RAG with Pinecone or Milvus for context-rich answers
- Build secure, observable, scalable AI microservices for cloud or on-prem deployment
- Test outputs, add guardrails, and monitor performance of LLMs and applications
- Explore advanced patterns, such as agentic workflows, multimodal LLMs, and practical image-processing use cases
Who This Book Is For
Java developers, architects, DevOps engineers, and technical leads who need to add AI features to new or existing enterprise systems. Data scientists and educators will also appreciate the code-first, Java-centric approach.
