Spring AI Training Course

1. Introduction to Spring AI

• Creating and configuring projects
• The function of prompts and prompt submission
• Writing initial tests
• Selecting a model
• Configuring the model
• Overview of Spring AI features

2. Interpreting responses

• Verifying the relevance of answers
• Assessing runtime accuracy

3. Prompting in depth

• Utilizing prompt templates
• Defining custom prompt templates
• Comprehending context
• The role and its significance
• Guiding response generation through options
• Streaming and formatting output
• Response metadata

4. Leveraging personal data and documents

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

5. Memory in AI

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

6. AI Tools

• Enabling tool support in applications
• Understanding tool capabilities
• Creating and deploying tools
• Using functions as tools

7. The Model Context Protocol (MCP)

• The purpose of MCP
• Working with an MCP Client
• Developing an MCP Server
• Integrating databases and tools into the MCP Server
• Understanding HTTP and SSE (Server-Sent Events) transport
• Exposing prompts and resources

8. Monitoring operations

• Activating actuator metrics
• Reviewing vector store operations
• Analyzing model interactions
• Token counting
• Aggregating data in Prometheus and building dashboards
• Tracing AI operations

9. Safeguarding generative AI

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

10. Common generative patterns

• Summarizing content
• Translating messages
• Analyzing sentiment

11. The role of Agents

• Defining an agent
• Implementing agentic workflows
• Chaining prompts, task routing, and parallelization
• Accessing agents via MCP