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Course Outline
1. Introduction to Spring AI
- Creating and configuring projects
- The function of prompts and their submission
- Developing initial tests
- Selecting a model
- Configuring the model
- Overview of Spring AI features
2. Interpreting responses
- Verifying the relevance of answers
- Assessing runtime accuracy
3. Deep dive into prompts
- Utilizing prompt templates
- Creating new prompt templates
- Grasping context
- Understanding roles and their significance
- Guiding response generation through options
- Streaming and output formatting
- Response metadata
4. Leveraging your data and documents
- Comprehending RAG (Retrieval-Augmented Generation)
- Configuring vector stores and ingesting documents
- Implementing an initial RAG solution
- Implementing RAG with an advisor
- Modular RAG functionalities
5. The significance of memory in AI
- The necessity of memory
- Adding and setting up memory for conversation support
- Conversation identification
- Supporting persistent memory
- Storing chat memory in vector stores
6. AI Tools
- Enabling tools in applications
- Understanding tool capabilities
- Developing and operationalizing tools
- Using functions as tools
7. The Model Context Protocol (MCP)
- The rationale behind 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. Monitoring operations
- Activating actuator metrics
- Reviewing vector store operations
- Monitoring model interactions
- Token counting
- Aggregating data in Prometheus and building dashboards
- Tracing AI operations
9. Safeguarding in generative AI
- Managing document access via RAG
- Securing tools
- Addressing adversarial prompting
- Moderating user input
10. Common generative patterns
- Summarizing content
- Translating messages
- Sentiment analysis
11. The role of Agents
- Defining an agent
- Implementing agentic workflows
- Chaining prompts, task routing, and parallelization
- Accessing agents via MCP
Requirements
Participants are expected to possess the following:
- Strong proficiency in Java programming
- Hands-on experience with Spring and Spring Boot
- Knowledge of developing and setting up Spring Boot applications
- Fundamental understanding of REST APIs and HTTP
- Basic grasp of JSON and application configuration
- Foundational knowledge of generative AI and Large Language Models (LLMs)
- Recommended familiarity with databases and data access concepts
- 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.
