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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