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Course Outline
1. Introduction to Spring AI
- Initiating projects and configuration
- The function of prompts and prompt submission
- Creating your first test
- Selecting the appropriate model
- Configuring the model
- An overview of Spring AI features
2. Interpreting responses
- Verifying the relevance of answers
- Evaluating runtime accuracy
3. Deep dive into prompts
- Utilizing prompt templates
- Crafting a new prompt template
- Comprehending context
- The role of context and its significance
- Guiding response generation via options
- Handling streaming and output formatting
- Reviewing response metadata
4. Leveraging your data and documents
- Grasping RAG (Retrieval-Augmented Generation)
- Establishing a vector store and ingesting documents
- Building a basic RAG implementation
- Implementing RAG with an advisor
- Modular RAG features
5. The importance of memory in AI
- The necessity of memory
- Incorporating and setting up memory for conversation support
- The concept of Conversation ID
- Implementing persistent memory
- Retaining chat memory in a vector store
6. AI Tools
- Enabling tools in an application
- Exploring tool capabilities
- Developing and deploying a tool
- Using functions as tools
7. The Model Context Protocol (MCP)
- The need for MCP
- Interacting with an MCP Client
- Developing an MCP Server
- Managing databases and tools for the MCP Server
- Understanding HTTP and SSE (Server-Sent Events) transport
- Publishing prompts and resources
8. Operational monitoring
- Activating actuator metrics
- Making sure of vector store operations
- Observing model interactions
- Token counting
- Integrating with Prometheus to build a dashboard
- Tracking AI operations
9. Security in generative AI
- Regulating documents accessed via RAG
- Protecting tools
- Countering adversarial prompting
- Moderating user inputs
10. Standard generative patterns
- Summarizing content
- Translating messages
- Analyzing sentiment
11. The role of Agents
- Defining an agent
- Building agentic workflows
- Chaining prompts, task routing, and parallelization
- Accessing agents through MCP
Requirements
To participate, you should have:
- A solid grasp of Java programming
- Hands-on experience with Spring and Spring Boot
- Knowledge of creating and setting up Spring Boot applications
- A fundamental understanding of REST APIs and HTTP
- A basic command of JSON and application configuration
- A foundational understanding of generative AI and Large Language Models (LLMs)
- Familiarity with database and data access principles is advised
- No previous experience with Spring AI, RAG, MCP, or AI agents is necessary
21 Hours
Testimonials (1)
Detailed information provided on the more advanced topics requested.
