From Java Dev to AI Engineer: Spring AI Fast Track | Udemy [Update 05/2026]
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Genre: eLearning[/center]

From Java Dev to AI Engineer: Spring AI Fast Track

What you'll learn:
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[*]Build Spring Boot applications powered by Spring AI
[*]Integrate Spring AI app with OpenAI, Ollama, Docker Model Runner, and AWS Bedrock
[*]Use prompt templates and prompt stuffing techniques
[*]Convert AI text responses to Java Beans, Lists, and Maps
[*]Understand how LLMs work internally with tokens and embeddings
[*]Implement Retrieval-Augmented Generation (RAG) with Spring AI
[*]Implement memory in chat apps using Spring AI advisors
[*]Teach LLMs to call tools exposed by Java methods
[*]Build both MCP clients and servers with Spring AI
[*]From Testing to Production - Making AI Answers Safer with Evaluators
[*]Observability in Spring AI - Metrics, Monitoring & Tracing
[*]Transcription, Speech, and Image Generation using Spring AI
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Are you ready to build AI-powered Java applications with real-world use cases? This hands-on course will teach you how to integrate cutting-edge AI capabilities into your Spring Boot applications using the Spring AI framework and OpenAI.
You'll master everything from building your first chat-based app to using Retrieval-Augmented Generation (RAG), Tool Calling, Structured Output Conversion, MCP (Model Context Protocol), and even Speech-to-Text, Text-to-Speech, and Image Generation - all using Java and Spring Boot.
From understanding how LLMs work to deploying production-ready AI features with observability, testing, and advisor-based safety, this course is packed with powerful demos, clean explanations, and practical techniques to bring intelligence to your backend.
Whether you're a Java developer, Spring enthusiast, or backend engineer exploring Generative AI, this course will guide you step-by-step with best practices and battle-tested code.
What You'll Learn:
Section 1: Welcome & Hello World with Spring AI
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[*]Understand the Spring AI framework and course roadmap
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[*]Build your first Spring Boot AI app using OpenAI
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[*]Deep dive into ChatModel and ChatClient APIs
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Section 2: Prompt Engineering & Structured Output
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[*]Use message roles, prompt templates, and stuffing techniques
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[*]Work with advisors to control AI behavior
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[*]Map AI responses to Java Beans, Lists, and Maps
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Section 3: Generative AI & LLM Fundamentals
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[*]Learn about tokens, embeddings, and how LLMs generate text
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[*]Understand attention, vocabulary, and model internals
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[*]Explore static vs positional embeddings and context windows
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Section 4: AI Memory with ChatHistory
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[*]Implement stateless-to-stateful conversations
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[*]Use MemoryAdvisors and Conversation IDs for per-user memory
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[*]Persist chat memory using JDBC and configure maxMessages
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Section 5: RAG - Retrieval-Augmented Generation
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[*]Set up a vector store (Qdrant) using Docker
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[*]Store and query document embeddings in Spring Boot
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[*]Use RetrievalAugmentationAdvisor to feed documents to AI
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Section 6: Tool Calling - Let AI Take Action
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[*]Enable tool invocation via LLMs
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[*]Build tools for real-time actions like querying time or database
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[*]Customize tool errors and return responses to users
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Section 7: Model Context Protocol (MCP)
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[*]Learn MCP architecture and communication patterns
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[*]Build MCP Clients and Servers using Spring AI
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[*]Integrate with GitHub's MCP Server and explore STDIO transport
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Section 8: Testing & Validating AI Outputs
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[*]Use RelevancyEvaluator and FactCheckingEvaluator
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[*]Test AI responses for correctness in dev and production
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[*]Add runtime safety checks with Spring Retry
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Section 9: Observability - Monitoring AI Operations
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[*]Enable Spring Boot Actuator metrics for AI
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[*]Set up Prometheus & Grafana dashboards
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[*]Trace AI behavior with OpenTelemetry and Jaeger
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Section 10: Speech & Image Generation
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[*]Convert voice to text with AI-powered transcription
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[*]Generate natural speech from text prompts
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[*]Turn prompts into images using the ImageModel
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Who this course is for:
Java and Spring Boot developers eager to integrate AI into real-world applications
Backend developers curious about LLMs, prompt engineering, and AI-powered workflows
Full Stack developers interested in adding AI capabilities to their microservices or APIs
Architects exploring Retrieval-Augmented Generation (RAG) and Tool Calling in Spring ecosystems
Professionals aiming to bring natural language interfaces to enterprise applications
Devs building chatbots, voice assistants, or image generation tools using Spring AI
Students and enthusiasts who want a practical, hands-on approach to Generative AI with Java

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