Spring AI Training Course

1. Getting Started with Spring AI

• Creating and configuring a new project
• The function of prompts and submitting them
• Writing an initial test case
• Selecting the appropriate model
• Configuring model parameters
• Overview of Spring AI features

2. Analyzing Responses

• Verifying the relevance of answers
• Evaluating runtime accuracy

3. Deep Dive into Prompts

• Utilizing prompt templates
• Creating custom prompt templates
• Comprehending context
• The significance of defining roles
• Guiding response generation via options
• Implementing streaming and output formatting
• Reviewing metadata within responses

4. Leveraging Your Data and Documents

• Concepts behind RAG (Retrieval-Augmented Generation)
• Establishing the vector store and ingesting documents
• Building a basic RAG implementation
• Implementing RAG using an advisor
• Exploring modular RAG functions

5. The Importance of Memory in AI

• The necessity of memory systems
• Integrating and configuring memory for conversations
• Managing conversation IDs
• Enabling persistent memory
• Saving chat memory in a vector store

6. Utilizing AI Tools

• Enabling tool support in applications
• Understanding the capabilities of tools
• Developing and executing tools
• Using functions as tools

7. Model Context Protocol (MCP)

• The rationale for MCP
• Interacting with an MCP Client
• Developing an MCP Server
• Integrating databases and tools 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 activities
• Tracking model interactions
• Counting tokens
• Aggregating data in Prometheus and building dashboards
• Tracing AI operations

9. Security in Generative AI

• Controlling document access via RAG
• Securing tool execution
• Counteracting adversarial prompting
• Moderating user inputs

10. Standard Generative Patterns

• Summarizing content
• Translating messages
• Analyzing sentiment

11. The Role of Agents

• Defining an AI agent
• Building agentic workflows
• Chaining prompts, routing tasks, and parallelizing
• Accessing agents via MCP