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Duration 21 hours
Course Outline
1. Introduction to Spring AI
- Setting up projects and configuration
- The function of prompts and their submission
- Creating an initial test
- Selecting a suitable model
- Configuring the model
- An overview of Spring AI capabilities
2. Understanding responses
- Verifying the relevance of answers
- Assessing accuracy at runtime
3. Prompt details
- Utilising prompt templates
- Creating a new prompt template
- Comprehending context
- The significance of role definitions
- Modifying response generation through options
- Streaming and formatting output
- Response metadata
4. Using your data and documents
- Grasping RAG (Retrieval-Augmented Generation)
- Establishing the vector store and ingesting documents
- Implementing a basic RAG solution
- Using advisors for RAG
- Modular RAG features
5. The role of memory in AI
- The necessity of memory
- Implementing and configuring memory for conversations
- Conversation identification
- Enabling persistent memory
- Storing chat history in a vector store
6. AI Tools
- Enabling tools in an application
- Understanding tool capabilities
- Developing and deploying tools
- Utilising functions as tools
7. The Model Context Protocol (MCP)
- The need 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. Monitoring operations
- Activating actuator metrics
- Reviewing vector store operations
- Analysing model interactions
- Token tracking
- Implementing Prometheus and creating dashboards
- Tracing AI operations
9. Safeguarding in generative AI
- Managing document access via RAG
- Securing tools
- Mitigating adversarial prompting
- Moderating user input
10. Common generative patterns
- Content summarisation
- Message translation
- Sentiment analysis
11. The role of Agents
- Defining an agent
- Implementing agentic workflows
- Chaining prompts, task routing, and parallelisation
- Accessing agents via MCP
Requirements
Participants are expected to have:
- A solid grasp of Java programming
- Practical experience with Spring and Spring Boot
- Familiarity with the development and configuration of Spring Boot applications
- A fundamental understanding of REST APIs and HTTP
- A basic understanding of JSON and application configuration
- A foundational knowledge of generative AI and Large Language Models (LLMs)
- Knowledge of databases and data access concepts is recommended
- No prior experience with Spring AI, RAG, MCP, or AI agents is necessary
Testimonials (1)
Detailed information provided on the more advanced topics requested.
