Spring AI Training Course

1. Getting Started with Spring AI

• Creating and configuring new projects
• The function of prompts and their submission
• Writing the first test case
• Selecting the appropriate model
• Configuring model parameters
• An overview of Spring AI's capabilities

2. Analyzing Model Responses

• Verifying the relevance of answers
• Evaluating runtime accuracy

3. Prompt Engineering In Depth

• Utilizing prompt templates
• Creating custom prompt templates
• Comprehending context
• The significance of role definition
• Guiding response generation through options
• Streaming and formatting output
• Extracting metadata from responses

4. Leveraging Proprietary Data and Documents

• Comprehending RAG (Retrieval-Augmented Generation)
• Setting up the vector store and ingesting documents
• Implementing a basic RAG pipeline
• Implementing RAG using an advisor
• Utilizing modular RAG features

5. The Importance of Memory in AI

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

6. Implementing AI Tools

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

7. The Model Context Protocol (MCP)

• The rationale for MCP
• Utilizing 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

• Enabling actuator metrics
• Monitoring vector store operations
• Tracking model interactions
• Counting tokens
• Integrating with Prometheus and building dashboards
• Tracing AI operations

9. Safeguards in Generative AI

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

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 parallelization
• Accessing agents via MCP