Spring AI Journey - Tutorials
Master Spring AI with our comprehensive tutorial series covering everything from core concepts to advanced integrations
Core AI Concepts
Understand the fundamental concepts of AI and how they apply to Spring AI development.
Spring AI Core – A Comprehensive Deep Dive
Master Spring AI architecture, prompt engineering, advisors, chat configuration, streaming, and structured output.
Generative AI & LLMs – A Friendly Family Get-Together
Explore the AI landscape from history to embeddings, understanding how LLMs process and generate text.
Memory Persistence and Conversational State in Spring AI
Build stateful AI conversations with chat memory, JDBC persistence, and token management strategies.
Conversational AI with Documents Using RAG
Build document chat systems with vector databases, smart chunking strategies, and RetrievalAugmentationAdvisor.
Mastering the Model Context Protocol (MCP)
Deep dive into MCP architecture, communication flow, Spring AI integration, and debugging with MCP Inspector.
Multimodal AI & Response Evaluation with Spring AI
Work with text, audio, images, evaluate LLM responses, and build voice/image generation capabilities.
Implementing Semantic Caching Using Spring AI
Learn how to implement semantic caching to optimize AI responses and reduce API costs.
Building Effective Agents with Spring AI
Create intelligent AI agents that can reason, plan, and execute complex tasks.
Exploring Model Context Protocol (MCP) With Spring AI
Deep dive into MCP and learn how to build interoperable AI tools and services.
Configuring Multiple LLMs in Spring AI
Configure and manage multiple Large Language Models in a single Spring AI application.
Streaming Response in Spring AI ChatClient
Implement real-time streaming responses for better user experience in chat applications.
MCP Authorization With Spring AI and OAuth2
Secure your MCP servers with OAuth2 authentication and authorization.
A Guide to OpenAI's Moderation Model in Spring AI
Implement content moderation using OpenAI's moderation API in your Spring AI applications.
Google Cloud and Spring AI
Integrate Google Cloud AI services including Vertex AI with your Spring applications.
Using Oracle Vector Database With Spring AI
Store and query vector embeddings using Oracle's vector database capabilities.
Senior Developer Interview Questions
Advanced Spring AI concepts: prompt engineering, RAG optimization, embedding strategies, caching, and production best practices.
Architect Level Interview Questions
System design with AI: LLM infrastructure, multi-model orchestration, cost optimization, scaling strategies, and enterprise patterns.