Java AI Engineer – Volume 2: RAG & Knowledge Systems

Connect LLMs with Enterprise Knowledge. Master RAG with Java.

20 Chapters | Enterprise Knowledge Engineering | Practical Java AI Development

Make AI Understand Your Enterprise Data!

An AI model is only as useful as the knowledge it can access.

Large Language Models are powerful, but they don't automatically know your company's private documents, internal knowledge bases, business processes, or constantly changing information.

This is where Retrieval-Augmented Generation (RAG) comes in.

Java AI Engineer – Volume 02: RAG & Knowledge Systems takes you beyond basic AI integrations and into the engineering of intelligent, knowledge-aware applications.

• Learn how to connect language models with private enterprise data, build high-precision retrieval pipelines, work with vector databases, and combine multiple search strategies to deliver more relevant, context-aware AI responses.
• Explore chunking strategies, embeddings, vector search, hybrid BM25 and dense retrieval, reranking, GraphRAG, and document-level security.
• Move beyond simple chatbot development and learn how to engineer enterprise knowledge systems using Java and modern AI technologies.

WHAT YOU'LL BUILD & MASTER

01. Connect AI with Enterprise Knowledge

Learn how to ground language models in private business data and build knowledge-aware AI applications that retrieve relevant information before generating responses.

02. Master Advanced Retrieval Techniques

Explore document chunking, embeddings, vector databases, hybrid search, reranking, and GraphRAG to improve the accuracy and relevance of retrieved information.

03. Build Secure and Reliable Knowledge Systems

Understand how to evaluate retrieval quality, enforce document-level access controls, and protect enterprise information while delivering context-aware AI experiences.

WHAT'S INSIDE THIS VOLUME?

✓ Retrieval-Augmented Generation (RAG): Connect LLMs with private enterprise knowledge.

✓ Document Chunking: Explore strategies for breaking documents into meaningful retrieval units.

✓ Vector Databases: Work with Pgvector, Qdrant, and Milvus.

✓ Embeddings & Vector Search: Understand semantic representations and similarity-based retrieval.

✓ Hybrid Search: Combine BM25 keyword search with dense vector retrieval.

✓ Reranking: Improve retrieval relevance by refining search results.

✓ GraphRAG: Explore graph-based approaches to connecting and retrieving knowledge.

✓ Document Security: Enforce document-level access and protect private enterprise information.

✓ Retrieval Evaluation: Measure and improve the quality of retrieved information.

WHY SHOULD JAVA DEVELOPERS READ THIS BOOK?

• Learn how to build enterprise knowledge systems using modern AI engineering concepts.

• Understand how to connect LLMs with private business data.

• Explore practical vector database technologies and advanced retrieval strategies.

• Go beyond basic chatbot development and understand real-world RAG architecture.

• Learn techniques to improve retrieval accuracy and reduce unsupported AI responses.

• Develop the knowledge required to build secure, context-aware AI applications.

Whether you're a Java developer, Spring Boot engineer, backend developer, or software architect exploring Generative AI, this volume helps you understand one of the most important building blocks of modern enterprise AI.

PRODUCT DETAILS

Series: Java AI Engineer

Volume: 02

Title: RAG & Knowledge Systems

Category: Enterprise Knowledge Engineering

Content: 20 Chapters

Format: Digital Ebook (PDF / ePub)

Additional Resources: Accompanying Source Code

Delivery: Instant Digital Download

Access: Lifetime Access

Updates: Free Lifetime Updates

YOUR DATA. YOUR KNOWLEDGE. YOUR AI.

An AI model can generate answers. But can it answer using your enterprise knowledge?

Learn how to connect the dots between private data, intelligent retrieval, and powerful language models.

Don't just build chatbots. Build intelligent systems that understand your business knowledge.

Take your Java AI engineering journey to the next level with Volume 02!

Published by Java Puzzle – Building the Next Generation of Java AI Engineers.