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Course Outline
1. Introduction to Spring AI
- Project initialization and configuration
- Prompt design and submission mechanisms
- Executing initial tests
- Model selection
- Model parameterization
- Overview of Spring AI features
2. Response analysis
- Validating response relevance
- Assessing runtime accuracy
3. Prompt engineering details
- Utilizing prompt templates
- Creating custom prompt templates
- Context management
- Defining and applying roles
- Configuring response generation options
- Output streaming and formatting
- Response metadata handling
4. Data and document integration
- Retrieval-Augmented Generation (RAG) fundamentals
- Vector store setup and document ingestion
- Implementing basic RAG workflows
- RAG implementation using advisors
- Modular RAG architecture
5. Memory in AI systems
- The necessity of memory
- Configuring conversational memory
- Conversation ID management
- Implementing persistent memory
- Vector store integration for chat memory
6. AI Tools
- Tools-enabled application architecture
- Tool capability assessment
- Tool development and deployment
- Function-based tools
7. Model Context Protocol (MCP)
- Rationale for MCP adoption
- MCP Client operations
- MCP Server development
- Database and tool integration for MCP Servers
- HTTP and SSE transport mechanisms
- Exposing prompts and resources
8. Operational monitoring
- Activating actuator metrics
- Vector store operation oversight
- Model interaction tracking
- Token usage accounting
- Prometheus integration and dashboard creation
- AI operation tracing
9. Security in generative AI
- Access control for RAG documents
- Tool security hardening
- Adversarial prompt mitigation
- User input moderation
10. Standard generative patterns
- Content summarization
- Message translation
- Sentiment analysis
11. Agent architectures
- Agent definition
- Agentic workflow implementation
- Prompt chaining, task routing, and parallelization
- Agent access via MCP
Requirements
Participants are expected to possess:
- Proficiency in Java programming
- Hands-on experience with Spring and Spring Boot
- Familiarity with developing and configuring Spring Boot applications
- Fundamental understanding of REST APIs and HTTP
- Fundamental understanding of JSON and application configuration
- Fundamental understanding of generative AI and Large Language Models (LLMs)
- Recommended familiarity with database and data access concepts
- No prior experience with Spring AI, RAG, MCP, or AI agents is required
21 Hours
Testimonials (1)
Detailed information provided on the more advanced topics requested.
