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Course Outline
1. Getting Started with Spring AI
- Project initialization and setup
- Understanding prompts and their submission
- Creating initial test cases
- Selecting the appropriate model
- Configuring model parameters
- Overview of Spring AI features
2. Analyzing Response Output
- Verifying the relevance of answers
- Assessing accuracy during runtime
3. Deep Dive into Prompting
- Implementing prompt templates
- Creating custom prompt templates
- Managing context within prompts
- Defining roles and their significance
- Controlling generation via options
- Stream processing and output formatting
- Accessing response metadata
4. Leveraging Custom Data and Documents
- Concepts of Retrieval-Augmented Generation (RAG)
- Vector store configuration and document ingestion
- Implementing a basic RAG solution
- Utilizing advisors for RAG
- Exploiting modular RAG components
5. Implementing Memory in AI Systems
- The necessity of conversational memory
- Configuring memory for dialogue support
- Managing conversation identifiers
- Implementing persistent memory solutions
- Persisting chat history in vector stores
6. Integrating AI Tools
- Enabling tool capabilities in applications
- Understanding tool functionalities
- Developing and executing custom tools
- Utilizing functions as tools
7. The Model Context Protocol (MCP)
- Rationale for adopting MCP
- Implementing an MCP client
- Developing an MCP server
- Connecting databases and tools to the MCP server
- Managing HTTP and SSE (Server-Sent Events) transport
- Exposing prompts and resources
8. Operational Monitoring
- Activating actuator metrics
- Monitoring vector store performance
- Tracking model interactions
- Analyzing token usage
- Integrating with Prometheus and building dashboards
- Distributed tracing of AI operations
9. Security in Generative AI
- Access control for RAG-retrieved documents
- Securing tool execution
- Defending against adversarial prompting
- Moderating user inputs
10. Standard Generative Patterns
- Summarizing content
- Translating messages
- Analyzing sentiment
11. Agentic Workflows
- Defining AI agents
- Building agentic workflows
- Chaining prompts, routing tasks, and parallel processing
- Accessing agents via MCP
Requirements
Learners are expected to possess the following skills:
- Strong proficiency in Java programming
- Hands-on experience with Spring and Spring Boot
- Experience in developing and configuring Spring Boot applications
- Fundamental knowledge of REST APIs and HTTP
- Basic understanding of JSON and application configuration
- Foundational knowledge of generative AI and Large Language Models (LLMs)
- Familiarity with databases and data access principles is advisable
- 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.
