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Duration 21 hours
Course Outline
1. Introduction to Spring AI
- Creating and configuring projects
- The function of prompts and their submission
- Writing initial tests
- Selecting an appropriate model
- Configuring model settings
- Overview of Spring AI capabilities
2. Interpreting responses
- Verifying answer relevance
- Assessing runtime accuracy
3. Prompt engineering details
- Utilizing prompt templates
- Defining custom prompt templates
- Understanding context usage
- The significance of the role parameter
- Influencing generation via options
- Streaming and formatting output
- Response metadata
4. Leveraging your data and documents
- Grasping RAG (Retrieval-Augmented Generation)
- Setting up vector stores and loading documents
- Implementing basic RAG
- Using RAG with advisors
- Modular RAG features
5. Memory in AI systems
- The necessity of memory
- Configuring memory for conversation support
- Managing conversation IDs
- Supporting persistent memory
- Storing chat history in vector stores
6. AI Tools
- Enabling tool capabilities in applications
- Understanding tool functions
- Developing and implementing tools
- Using functions as tools
7. Model Context Protocol (MCP)
- The purpose of MCP
- Operating with an MCP Client
- Developing an MCP Server
- Integrating databases and tools for the MCP Server
- Understanding HTTP and SSE (Server-Sent Events) transports
- Exposing prompts and resources
8. Operational monitoring
- Activating actuator metrics
- Monitoring vector store operations
- Tracking model interactions
- Counting tokens
- Implementing Prometheus and creating dashboards
- Tracing AI operations
9. Generative AI safeguarding
- Controlling RAG-accessed documents
- Securing AI tools
- Mitigating adversarial prompting
- Moderating user input
10. Standard generative patterns
- Summarizing content
- Translating messages
- Analyzing sentiment
11. The function of Agents
- Defining an agent
- Building agentic workflows
- Chaining prompts, routing tasks, and parallelizing
- Accessing agents via MCP
Requirements
To fully benefit from this course, participants should possess:
- Solid proficiency in Java programming
- Practical working experience with Spring and Spring Boot
- Knowledge of how to build and configure Spring Boot applications
- A foundational understanding of REST APIs and HTTP
- Basic familiarity with JSON and application configuration
- An introductory grasp of generative AI and Large Language Models (LLMs)
- Recommended familiarity with database concepts and data access
- No prior experience with Spring AI, RAG, MCP, or AI agents is necessary
Testimonials (1)
Detailed information provided on the more advanced topics requested.