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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

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