Spring AI Training Course

1. Introduction to Spring AI

• Creating and configuring projects
• The role of prompts and prompt submission
• Writing your first test
• Selecting a model
• Configuring the model
• Overview of Spring AI capabilities

2. Understanding responses

• Verifying relevant answers
• Assessing runtime accuracy

3. Prompt details

• Utilizing prompt templates
• Defining new prompt templates
• Understanding context
• Importance of roles
• Influencing response generation via options
• Streaming and output formatting
• Response metadata

4. Utilizing your data and documents

• Understanding RAG (Retrieval-Augmented Generation)
• Setting up vector stores and loading documents
• Initial RAG implementation
• Implementing RAG using an advisor
• Modular RAG features

5. The role of memory in AI

• The necessity of memory
• Adding and configuring memory for conversations
• Conversation IDs
• Supporting persistent memory
• Storing chat memory in vector stores

6. AI Tools

• Enabling tools in applications
• Understanding tool capabilities
• Developing and applying tools
• Using functions as tools

7. The Model Context Protocol (MCP)

• The purpose of MCP
• Working with MCP Clients
• Developing MCP Servers
• Databases and tools for MCP Servers
• Understanding HTTP and SSE (Server-Sent Events) transport
• Exposing prompts and resources

8. Monitoring operations

• Enabling actuator metrics
• Reviewing vector store operations
• Analyzing model interactions
• Token counting
• Integrating with Prometheus and building dashboards
• Tracing AI operations

9. Safeguarding in generative AI

• Controlling document access via RAG
• Securing tools
• Mitigating adversarial prompting
• Moderating user input

10. Common generative patterns

• Content summarization
• Message translation
• Sentiment analysis

11. The role of Agents

• Definition of an agent
• Implementing agentic workflows
• Chaining prompts, task routing, and parallelization
• Agent access via MCP