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Course Outline
1. Introduction to Spring AI
- Project initialization and configuration
- The function of prompts and prompt submission
- Writing the first test
- Selecting an appropriate model
- Configuring the model
- Overview of Spring AI features
2. Interpreting responses
- Methods for verifying relevant answers
- Evaluating accuracy at runtime
3. Detailed prompt engineering
- Utilizing prompt templates
- Creating a new prompt template
- Comprehending context
- Understanding its significance
- Guiding response generation via options
- Streaming and formatting output
- Analyzing response metadata
4. Leveraging your data and documents
- Comprehending RAG (Retrieval-Augmented Generation)
- Setting up the vector store and ingesting documents
- Implementing basic RAG
- Implementing RAG with an advisor
- Modular RAG features
5. The importance of memory in AI
- The necessity of memory
- Implementing and configuring memory for conversation support
- Managing conversation IDs
- Enabling persistent memory
- Storing chat memory in a vector store
6. AI Tools
- Building applications with tool support
- Understanding tool capabilities
- Writing and deploying tools
- Using functions as tools
7. The Model Context Protocol (MCP)
- The necessity of MCP
- Working with an MCP Client
- Developing an MCP Server
- 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 operations
- Observing model interactions
- Token counting
- Integrating with Prometheus and creating dashboards
- Tracing AI operations
9. Security in generative AI
- Controlling document access via RAG
- Securing tools
- Mitigating adversarial prompting
- Moderating user input
10. Common 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
Requirements
Participants are expected to have the following background:
- Solid proficiency in Java programming
- Practical experience with Spring and Spring Boot
- Knowledge of building and configuring Spring Boot applications
- A fundamental grasp of REST APIs and HTTP
- Basic understanding of JSON and application configuration
- A basic understanding of generative AI and Large Language Models (LLMs)
- Familiarity with databases and data access concepts is advised
- No prior experience with Spring AI, RAG, MCP, or AI agents is necessary
21 Hours
Testimonials (1)
Detailed information provided on the more advanced topics requested.
