Spring AI Training Course

1. Getting Started with Spring AI

• Initializing projects and setting up configurations
• The importance of prompts and their submission
• Creating your first test case
• Selecting the appropriate model
• Configuring model parameters
• An overview of Spring AI's core features

2. Interpreting Model Responses

• Validating the relevance of answers
• Assessing accuracy during runtime

3. Prompt Engineering Deep Dive

• Leveraging prompt templates
• Creating custom prompt templates
• Comprehending contextual nuances
• The significance of defining roles
• Guiding response generation through configuration options
• Implementing streaming and output formatting
• Utilizing metadata within responses

4. Integrating Proprietary Data and Documents

• Concepts of RAG (Retrieval-Augmented Generation)
• Configuring vector stores and ingesting documents
• Building an initial RAG implementation
• Implementing RAG with an advisor
• Exploring modular RAG features

5. The Importance of Memory in AI

• The necessity of memory in AI systems
• Implementing and configuring memory for conversational flow
• Managing conversation IDs
• Enabling persistent memory support
• Persisting chat memory in vector stores

6. Leveraging AI Tools

• Building tool-enabled applications
• Understanding the capabilities of AI tools
• Developing and deploying tools
• Utilizing functions as tools

7. Exploring the Model Context Protocol (MCP)

• The rationale behind MCP
• Interacting with MCP Clients
• Developing MCP Servers
• Integrating databases and tools for MCP Servers
• Understanding HTTP and SSE (Server-Sent Events) transports
• Exposing prompts and resources

8. Operational Monitoring

• Activating actuator metrics
• Mining vector store operations
• Analyzing model interactions
• Tracking token usage
• Aggregating data in Prometheus and building dashboards
• Tracing AI operation flows

9. Security and Safeguards in Generative AI

• Restricting document access via RAG
• Protecting AI tools
• Mitigating adversarial prompting
• Moderating user inputs

10. Standard Generative AI Patterns

• Summarizing content
• Translating messages
• Analyzing sentiment

11. The Role of AI Agents

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