[Udemy, Madan Reddy, Eazy Bytes] From Java Dev to AI Engineer: Spring AI Fast Track [8/2025, ENG]

Section 1: Welcome & Hello World with Spring AI

1. Understand the Spring AI framework and course roadmap
   
2. Build your first Spring Boot AI app using OpenAI
   
3. Deep dive into ChatModel and ChatClient APIs

Section 2: Prompt Engineering & Structured Output

1. Use message roles, prompt templates, and stuffing techniques
   
2. Work with advisors to control AI behavior
   
3. Map AI responses to Java Beans, Lists, and Maps

Section 3: Generative AI & LLM Fundamentals

1. Learn about tokens, embeddings, and how LLMs generate text
   
2. Understand attention, vocabulary, and model internals
   
3. Explore static vs positional embeddings and context windows

Section 4: AI Memory with ChatHistory

1. Implement stateless-to-stateful conversations
   
2. Use MemoryAdvisors and Conversation IDs for per-user memory
   
3. Persist chat memory using JDBC and configure maxMessages

Section 5: RAG – Retrieval-Augmented Generation

1. Set up a vector store (Qdrant) using Docker
   
2. Store and query document embeddings in Spring Boot
   
3. Use RetrievalAugmentationAdvisor to feed documents to AI

Section 6: Tool Calling – Let AI Take Action

1. Enable tool invocation via LLMs
   
2. Build tools for real-time actions like querying time or database
   
3. Customize tool errors and return responses to users

Section 7: Model Context Protocol (MCP)

1. Learn MCP architecture and communication patterns
   
2. Build MCP Clients and Servers using Spring AI
   
3. Integrate with GitHub’s MCP Server and explore STDIO transport

Section 8: Testing & Validating AI Outputs

1. Use RelevancyEvaluator and FactCheckingEvaluator
   
2. Test AI responses for correctness in dev and production
   
3. Add runtime safety checks with Spring Retry

Section 9: Observability – Monitoring AI Operations

1. Enable Spring Boot Actuator metrics for AI
   
2. Set up Prometheus & Grafana dashboards
   
3. Trace AI behavior with OpenTelemetry and Jaeger

Section 10: Speech & Image Generation

1. Convert voice to text with AI-powered transcription
   
2. Generate natural speech from text prompts
   
3. Turn prompts into images using the ImageModel

Who this course is for:

1. Java and Spring Boot developers eager to integrate AI into real-world applications
   
2. Backend developers curious about LLMs, prompt engineering, and AI-powered workflows
   
3. Full Stack developers interested in adding AI capabilities to their microservices or APIs
   
4. Architects exploring Retrieval-Augmented Generation (RAG) and Tool Calling in Spring ecosystems
   
5. Professionals aiming to bring natural language interfaces to enterprise applications
   
6. Devs building chatbots, voice assistants, or image generation tools using Spring AI
   
7. Students and enthusiasts who want a practical, hands-on approach to Generative AI with Java

The 2025/8 version has increased the number of lessons by 51 and the duration increased by 7 hours 4 minutes compared to 2025/7.

General
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