🚀 Spring AI & Generative AI-Complete Concept Notes with Ajay kumar

🚀 Spring AI & Generative AI — Complete Concept Notes

A practical, concept-focused PDF for Java & Spring Boot developers who want to understand Generative AI and Spring AI without getting lost in complex theory.

📚 What You’ll Learn

• Spring AI fundamentals
• ChatClient vs ChatModel
• Prompts & Message Roles
• ChatOptions & Advisors
• LLM fundamentals
• Tokens & Tokenization
• Embeddings & Vector Similarity
• Attention & Context Windows
• Chat Memory & Memory Strategies
• RAG (Retrieval-Augmented Generation)
• Chunking & Vector Stores
• Similarity Search & Top-K
• Advanced RAG
• Semantic Caching
• Tool Calling
• ToolCallingManager & Tool Context
• AI Agents & Agentic AI
• MCP (Model Context Protocol)
• Production AI architecture
• RAG vs Memory vs Cache vs Tools vs Agents
• Recommended AI Engineering learning path

🎯 Who Is This For?

• Java Developers
• Spring Boot Developers
• Backend Engineers entering AI
• Developers learning Spring AI
• Beginners exploring LLMs, RAG & AI Agents
• Developers preparing for AI/GenAI interviews

🧠 The Core Mental Models

LLM + RAG = Knowledge

LLM + Tools = Actions

LLM + Memory = Conversation

LLM + Cache = Performance

LLM + Agent = Planning & Execution

MCP = Standardized AI Integration