Get in Touch

Course Outline

1. Introduction to Spring AI

  • Creating and configuring projects
  • The significance of prompts and their submission
  • Writing an initial test
  • Selecting a model
  • Configuring the model
  • Overview of Spring AI features

2. Analyzing responses

  • Verifying the relevance of answers
  • Assessing runtime accuracy

3. Deep dive into prompts

  • Utilizing prompt templates
  • Creating new prompt templates
  • Comprehending context
  • The importance of roles
  • Guiding response generation through options
  • Streaming and formatting output
  • Response metadata

4. Utilizing proprietary data and documents

  • Grasping RAG (Retrieval-Augmented Generation)
  • Configuring vector stores and ingesting documents
  • Initial RAG implementation
  • Implementing RAG with an advisor
  • Modular RAG functionalities

5. The importance of memory in AI

  • The necessity of memory
  • Integrating and configuring memory for conversations
  • Conversation IDs
  • Implementing persistent memory
  • Storing chat history in vector stores

6. AI Tools

  • Enabling tools in applications
  • Understanding tool capabilities
  • Developing and deploying tools
  • Functions as tools

7. The Model Context Protocol (MCP)

  • The need for MCP
  • Implementing 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. Operational monitoring

  • Activating actuator metrics
  • Monitoring vector store operations
  • Tracking model interactions
  • Token counting
  • Integrating with Prometheus and building dashboards
  • Tracing AI operations

9. Security in generative AI

  • Controlling documents accessed via RAG
  • Securing tools
  • Defending against adversarial prompting
  • Moderating user input

10. Standard generative patterns

  • Summarizing content
  • Translating messages
  • Performing sentiment analysis

11. The function of Agents

  • Defining an agent
  • Building agentic workflows
  • Chaining prompts, task routing, and parallelization
  • Accessing agents via MCP

Requirements

Participants are expected to have:

  • Solid expertise in Java programming
  • Hands-on experience with Spring and Spring Boot
  • Proficiency in developing and configuring Spring Boot applications
  • Fundamental knowledge of REST APIs and HTTP
  • Basic understanding of JSON and application configuration
  • Introductory knowledge of generative AI and Large Language Models (LLMs)
  • Recommended familiarity with database and data access concepts
  • No previous exposure to Spring AI, RAG, MCP, or AI agents is necessary
 21 Hours

Number of participants


Price per participant

Testimonials (1)

Upcoming Courses

Related Categories