Build AI Apps with Spring AI, OpenAI, RAG, MCP, AI Testing, Observability, Speech & Image Generation
What you'll learn
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
Requirements
Knowledge on Java, Spring Boot is mandatory
Description
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 AIUnderstand the Spring AI framework and course roadmapBuild your first Spring Boot AI app using OpenAIDeep dive into ChatModel and ChatClient APIsSection 2: Prompt Engineering & Structured OutputUse message roles, prompt templates, and stuffing techniquesWork with advisors to control AI behaviorMap AI responses to Java Beans, Lists, and MapsSection 3: Generative AI & LLM FundamentalsLearn about tokens, embeddings, and how LLMs generate textUnderstand attention, vocabulary, and model internalsExplore static vs positional embeddings and context windowsSection 4: AI Memory with ChatHistoryImplement stateless-to-stateful conversationsUse MemoryAdvisors and Conversation IDs for per-user memoryPersist chat memory using JDBC and configure maxMessagesSection 5: RAG - Retrieval-Augmented GenerationSet up a vector store (Qdrant) using DockerStore and query document embeddings in Spring BootUse RetrievalAugmentationAdvisor to feed documents to AISection 6: Tool Calling - Let AI Take ActionEnable tool invocation via LLMsBuild tools for real-time actions like querying time or databaseCustomize tool errors and return responses to usersSection 7: Model Context Protocol (MCP)Learn MCP architecture and communication patternsBuild MCP Clients and Servers using Spring AIIntegrate with GitHub's MCP Server and explore STDIO transportSection 8: Testing & Validating AI OutputsUse RelevancyEvaluator and FactCheckingEvaluatorTest AI responses for correctness in dev and productionAdd runtime safety checks with Spring RetrySection 9: Observability - Monitoring AI OperationsEnable Spring Boot Actuator metrics for AISet up Prometheus & Grafana dashboardsTrace AI behavior with OpenTelemetry and JaegerSection 10: Speech & Image GenerationConvert voice to text with AI-powered transcriptionGenerate natural speech from text promptsTurn prompts into images using the ImageModel
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

Build AI Apps with Spring AI, OpenAI, RAG, MCP, AI Testing, Observability, Speech & Image Generation

What you'll learn
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

Requirements
Knowledge on Java, Spring Boot is mandatory

Description
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 AIUnderstand the Spring AI framework and course roadmapBuild your first Spring Boot AI app using OpenAIDeep dive into ChatModel and ChatClient APIsSection 2: Prompt Engineering & Structured OutputUse message roles, prompt templates, and stuffing techniquesWork with advisors to control AI behaviorMap AI responses to Java Beans, Lists, and MapsSection 3: Generative AI & LLM FundamentalsLearn about tokens, embeddings, and how LLMs generate textUnderstand attention, vocabulary, and model internalsExplore static vs positional embeddings and context windowsSection 4: AI Memory with ChatHistoryImplement stateless-to-stateful conversationsUse MemoryAdvisors and Conversation IDs for per-user memoryPersist chat memory using JDBC and configure maxMessagesSection 5: RAG - Retrieval-Augmented GenerationSet up a vector store (Qdrant) using DockerStore and query document embeddings in Spring BootUse RetrievalAugmentationAdvisor to feed documents to AISection 6: Tool Calling - Let AI Take ActionEnable tool invocation via LLMsBuild tools for real-time actions like querying time or databaseCustomize tool errors and return responses to usersSection 7: Model Context Protocol (MCP)Learn MCP architecture and communication patternsBuild MCP Clients and Servers using Spring AIIntegrate with GitHub's MCP Server and explore STDIO transportSection 8: Testing & Validating AI OutputsUse RelevancyEvaluator and FactCheckingEvaluatorTest AI responses for correctness in dev and productionAdd runtime safety checks with Spring RetrySection 9: Observability - Monitoring AI OperationsEnable Spring Boot Actuator metrics for AISet up Prometheus & Grafana dashboardsTrace AI behavior with OpenTelemetry and JaegerSection 10: Speech & Image GenerationConvert voice to text with AI-powered transcriptionGenerate natural speech from text promptsTurn prompts into images using the ImageModel

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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