Spring AI Training Course

1. Introduction to Spring AI

• Creating and configuring projects
• The significance of prompts and their submission
• Writing an initial test
• Selecting a model
• Configuring the model
• An overview of Spring AI capabilities

2. Analyzing responses

• Verifying the relevance of answers
• Evaluating runtime accuracy

3. Detailed prompting strategies

• Utilizing prompt templates
• Defining new prompt templates
• Comprehending context
• The role of context and its importance
• Influencing response generation via options
• Streaming and formatting output
• Response metadata

4. Leveraging your data and documents

• Comprehending RAG (Retrieval-Augmented Generation)
• Configuring the vector store and ingesting documents
• Implementing basic RAG
• RAG implementation using an advisor
• Modular RAG features

5. The importance of memory in AI

• The necessity of memory
• Integrating and configuring memory for conversations
• Managing conversation IDs
• Supporting persistent memory
• Storing chat memory in a vector store

6. AI Tools

• Building tools-enabled applications
• Exploring tool capabilities
• Developing and deploying tools
• Using functions as tools

7. The Model Context Protocol (MCP)

• The need for MCP
• Interacting with an MCP Client
• Developing the MCP Server
• Database and tool integration for the MCP Server
• Understanding HTTP and SSE (Server-Sent Events) transport
• Exposing prompts and resources

8. Operational monitoring

• Activating actuator metrics
• Monitoring vector store operations
• Tracking model interactions
• Token counting
• Integrating with Prometheus and building dashboards
• Tracing AI operations

9. Safeguards in generative AI

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

10. Standard generative patterns

• Content summarization
• Message translation
• Sentiment analysis

11. The function of Agents

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