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Course Outline
1. Introduction to Spring AI
- Creating and setting up projects
- The function of prompts and submitting them
- Creating an initial test
- Selecting a model
- Configuring the model
- Overview of Spring AI features
2. Analyzing responses
- Verifying the relevance of answers
- Assessing accuracy during runtime
3. Deep dive into prompts
- Applying prompt templates
- Creating a new prompt template
- Comprehending context
- Significance and role of context
- Guiding response generation through options
- Streaming and structuring output
- Response metadata
4. Leveraging your data and documents
- Concepts of RAG (Retrieval-Augmented Generation)
- Setting up vector stores and ingesting documents
- Initial RAG implementation
- Implementing RAG with an advisor
- Modular RAG features
5. The significance of memory in AI
- The necessity of memory
- Implementing and configuring memory for conversations
- Managing conversation IDs
- Enabling persistent memory
- Saving chat memory in vector stores
6. AI Tools
- Enabling tool support in applications
- Understanding tool capabilities
- Developing and deploying tools
- Utilizing functions as tools
7. Model Context Protocol (MCP)
- The need for MCP
- Interacting with an MCP Client
- Developing the MCP Server
- Integrating databases and tools for the MCP Server
- Understanding HTTP and SSE (Server-Sent Events) transport
- Exposing prompts and resources
8. Monitoring operations
- Activating actuator metrics
- Reviewing vector store operations
- Analyzing model interactions
- Counting tokens
- Integrating data into Prometheus and building dashboards
- Tracing AI activities
9. Security in generative AI
- Regulating document access via RAG
- Protecting tools
- Defending against adversarial prompting
- Moderating user input
10. Standard generative patterns
- Summarizing content
- Translating messages
- Analyzing sentiment
11. The function of Agents
- Definition of an agent
- Building agentic workflows
- Chaining prompts, task routing, and parallel execution
- Accessing agents via MCP
Requirements
Participants are expected to possess the following:
- Solid proficiency in Java programming
- Practical hands-on experience with Spring and Spring Boot
- Acquaintance with the process of building and configuring Spring Boot applications
- Fundamental understanding of REST APIs and HTTP
- Basic knowledge of JSON and application configuration
- Foundational understanding of generative AI and Large Language Models (LLMs)
- Knowledge of databases and data access principles is beneficial
- 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.
