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Course Outline
1. Introduction to Spring AI
- Creating and configuring projects
- Understanding the role of prompts and prompt submission
- Writing an initial test
- Selecting an appropriate model
- Configuring the model
- Overview of Spring AI capabilities
2. Interpreting responses
- Verifying the relevance of answers
- Evaluating runtime accuracy
3. Detailed prompt engineering
- Utilising prompt templates
- Creating new prompt templates
- Understanding context
- The importance of roles
- Influencing response generation via options
- Streaming and formatting output
- Interpreting response metadata
4. Leveraging your data and documents
- Understanding RAG (Retrieval-Augmented Generation)
- Setting up the vector store and loading documents
- Implementing a basic RAG solution
- Implementing RAG using an advisor
- Exploring modular RAG capabilities
5. The importance of memory in AI
- The necessity of memory in AI systems
- Configuring memory to support conversations
- Managing conversation IDs
- Supporting persistent memory
- Storing chat memory in a vector store
6. AI Tools
- Building tool-enabled applications
- Understanding tool capabilities
- Developing and deploying tools
- Utilising functions as tools
7. The Model Context Protocol (MCP)
- The need for MCP
- Working 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. Operational monitoring
- Enabling actuator metrics
- Monitoring vector store operations
- Tracking model interactions
- Counting tokens
- Implementing Prometheus and creating dashboards
- Tracing AI operations
9. Safeguarding in generative AI
- Controlling 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
To succeed in this course, participants are expected to have the following:
- Solid knowledge of Java programming
- Practical experience working with Spring and Spring Boot
- Experience in building and configuring Spring Boot applications
- Fundamental understanding of REST APIs and HTTP
- Fundamental understanding of JSON and application configuration
- Basic 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.
