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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
Testimonials (1)
Detailed information provided on the more advanced topics requested.