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Generative AI-driven application dev...

Sahu, Satej Kumar.

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Generative AI-driven application development with Java = leveraging large language models in modern Java applications /

Record Type:	Electronic resources : Monograph/item
Title/Author:	Generative AI-driven application development with Java/ by Satej Kumar Sahu.
Reminder of title:	leveraging large language models in modern Java applications /
Author:	Sahu, Satej Kumar.
Published:	Berkeley, CA :Apress : : 2025.,
Description:	xxv, 698 p. :ill., digital ;24 cm.
[NT 15003449]:	1: Megabrains 101: Generative AI & LLMs Unboxed -- 2: First Contact: "Hello, LLM" with Spring Boot -- 3: Bring Your Own Model: Self-Hosting with Ollama -- 4: Power Tools: LangChain4j Quick-Start -- 5: Integrating LLMs with Java Applications -- 6: From Chatty to Clever: Retrieval-Augmented Generation -- 7: Spring AI Ninja Moves -- 8: Prompt Alchemy: Patterns that Make Models Look Smarter -- 9: Swiss-Army LLMs: Tool Calls in Spring AI -- 10: Agents Assemble! Building Autonomous Workflows -- 11: The Transformer Saga-From Attention to Fine-Tuning -- 12: Does It Even Work? Testing & Evaluating LLM Apps -- 13: Cloud Power-Ups-Bedrock, Vertex & Azure OpenAI -- 14: Talking in Protocols: The MCP Revolution -- 15: Quarkus + LangChain4j: Lightning-Fast Gen AI -- 16: Jlama & Friends: Hosting Models the Java Way -- 17: Seeing Is Believing: Multimodal LLMs & Image Hacking -- 18: Native-Speed Machine Learning in Java: DJL, ONNX & JNI -- 19: Can You See Me Now? Observability for LLM Pipelines -- 20: Architectures of Tomorrow: From Monoliths to Modular Minds.
Contained By:	Springer Nature eBook
Subject:	Java (Computer program language) -
Online resource:	https://doi.org/10.1007/979-8-8688-1609-3
ISBN:	9798868816093

Generative AI-driven application development with Java = leveraging large language models in modern Java applications /
Sahu, Satej Kumar.

Generative AI-driven application development with Javaleveraging large language models in modern Java applications /[electronic resource] :by Satej Kumar Sahu. - Berkeley, CA :Apress :2025. - xxv, 698 p. :ill., digital ;24 cm.

1: Megabrains 101: Generative AI & LLMs Unboxed -- 2: First Contact: "Hello, LLM" with Spring Boot -- 3: Bring Your Own Model: Self-Hosting with Ollama -- 4: Power Tools: LangChain4j Quick-Start -- 5: Integrating LLMs with Java Applications -- 6: From Chatty to Clever: Retrieval-Augmented Generation -- 7: Spring AI Ninja Moves -- 8: Prompt Alchemy: Patterns that Make Models Look Smarter -- 9: Swiss-Army LLMs: Tool Calls in Spring AI -- 10: Agents Assemble! Building Autonomous Workflows -- 11: The Transformer Saga-From Attention to Fine-Tuning -- 12: Does It Even Work? Testing & Evaluating LLM Apps -- 13: Cloud Power-Ups-Bedrock, Vertex & Azure OpenAI -- 14: Talking in Protocols: The MCP Revolution -- 15: Quarkus + LangChain4j: Lightning-Fast Gen AI -- 16: Jlama & Friends: Hosting Models the Java Way -- 17: Seeing Is Believing: Multimodal LLMs & Image Hacking -- 18: Native-Speed Machine Learning in Java: DJL, ONNX & JNI -- 19: Can You See Me Now? Observability for LLM Pipelines -- 20: Architectures of Tomorrow: From Monoliths to Modular Minds.

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.

ISBN: 9798868816093

Standard No.: 10.1007/979-8-8688-1609-3doiSubjects--Topical Terms:

522522
Java (Computer program language)

LC Class. No.: QA76.73.J38

Dewey Class. No.: 005.133

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505 0 $a 1: Megabrains 101: Generative AI & LLMs Unboxed -- 2: First Contact: "Hello, LLM" with Spring Boot -- 3: Bring Your Own Model: Self-Hosting with Ollama -- 4: Power Tools: LangChain4j Quick-Start -- 5: Integrating LLMs with Java Applications -- 6: From Chatty to Clever: Retrieval-Augmented Generation -- 7: Spring AI Ninja Moves -- 8: Prompt Alchemy: Patterns that Make Models Look Smarter -- 9: Swiss-Army LLMs: Tool Calls in Spring AI -- 10: Agents Assemble! Building Autonomous Workflows -- 11: The Transformer Saga-From Attention to Fine-Tuning -- 12: Does It Even Work? Testing & Evaluating LLM Apps -- 13: Cloud Power-Ups-Bedrock, Vertex & Azure OpenAI -- 14: Talking in Protocols: The MCP Revolution -- 15: Quarkus + LangChain4j: Lightning-Fast Gen AI -- 16: Jlama & Friends: Hosting Models the Java Way -- 17: Seeing Is Believing: Multimodal LLMs & Image Hacking -- 18: Native-Speed Machine Learning in Java: DJL, ONNX & JNI -- 19: Can You See Me Now? Observability for LLM Pipelines -- 20: Architectures of Tomorrow: From Monoliths to Modular Minds.
520 $a 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.
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650 0 $a Artificial intelligence. $3 516317
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650 2 4 $a Programming Techniques. $3 892496
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