Mastering Spring AI with Java

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Module 1 — Foundations of AI for Java Developers

Build foundational understanding of AI, ML, LLMs, and how they apply to applications.

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Learning objectives Understand AI vs traditional software Learn basics of ML and LLMs Understand tokens, prompts, and responses Know limitations and real-world use cases

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Module 2 — Introduction to Spring AI

Learn what Spring AI is and how it fits into the Spring Boot ecosystem.

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Learning objectives Understand Spring AI and its architecture Learn core components and model providers Set up a Spring Boot project with Spring AI

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Module 3 — OpenAI Integration and API Setup

Configure OpenAI API keys and make your first AI calls from Spring Boot.

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Learning objectives Understand OpenAI API and account setup Manage API keys and application.properties Make first API call and handle errors

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Module 4 — Building Your First AI Chat Application

Build a simple chat endpoint and integrate AI responses in REST controllers.

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Learning objectives Use the chat client and message types Send prompts from REST and return AI responses Test with Postman and improve output quality

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Module 5 — Prompt Engineering

Write effective prompts and control model behavior with templates and instructions.

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Learning objectives Learn prompt engineering and role-based prompting Use prompt templates and dynamic construction Avoid common mistakes and control behavior

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Module 6 — Structured Output Handling

Generate and map AI output to Java objects and design clean API contracts.

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Learning objectives Generate JSON from AI and map to Java objects Use DTOs and handle parsing errors Validate responses and design API contracts

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Module 7 — Chat Memory and Context Management

Implement stateful conversations with message and database-backed memory.

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Learning objectives Understand stateless vs stateful and message window memory Use JDBC chat memory and persist conversations Manage and clear context

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Module 8 — Embeddings and Vector Databases

Learn embeddings, vector stores, semantic search, and RAG with Spring AI.

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Learning objectives Understand embeddings and cosine similarity Use vector databases and semantic search Introduction to RAG and Spring AI integration

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Module 9 — Metadata Filtering and Advanced Retrieval

Add metadata to documents and filter results with conditions and operators.

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Learning objectives Use metadata in AI systems and add to documents Filter with AND, OR, NOT and numeric conditions Apply search optimization techniques

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Module 10 — Tool Calling and Function Execution

Let AI trigger Java methods and combine tool calling with chat memory.

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Learning objectives Understand tool calling and design tool interfaces Register tools in Spring AI and invoke programmatically Combine with chat memory for real-world use cases

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Module 11 — Multimodal AI (Image, Audio, and More)

Work with image generation, TTS, STT, and binary data in Spring Boot.

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Learning objectives Use multimodal models for image, TTS, STT Handle binary data in Spring Boot Apply to multimodal use cases

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Module 12 — Running AI Models Locally

Run models locally with Ollama and compare cloud vs local deployment.

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Learning objectives Understand why run locally and use Ollama Connect Spring AI to local models Compare performance and cost optimization

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Module 13 — Monitoring, Logging, and Production Readiness

Make AI applications observable, secure, and ready for production deployment.

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Learning objectives Implement observability, logging, and token monitoring Handle rate limits and secure API keys Optimize performance and deploy