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Duration 21 hours
Course Outline
1. Introduction to Spring AI
- Setting up project creation and configuration
- The function of prompts and their submission
- Writing your first test
- Selecting an appropriate model
- Configuring model parameters
- An overview of Spring AI capabilities
2. Understanding responses
- Methods for verifying answer relevance
- Evaluating accuracy at runtime
3. Detailed prompt engineering
- Utilizing prompt templates
- Creating new prompt templates
- Understanding context management
- Defining roles and their significance
- Shaping response generation via options
- Implementing streaming and output formatting
- Interpreting response metadata
4. Leveraging your data and documents
- Grasping RAG (Retrieval-Augmented Generation)
- Setting up the vector store and importing documents
- Implementing a basic RAG solution
- Building RAG with an advisor
- Exploring modular RAG features
5. The importance of memory in AI
- The need for conversation memory
- Integrating and configuring memory for conversations
- Managing conversation IDs
- Implementing persistent memory
- Storing chat history in a vector store
6. AI Tools
- Enabling tools in applications
- Exploring tool capabilities
- Developing and deploying tools
- Using functions as tools
7. The Model Context Protocol (MCP)
- The necessity of MCP
- Working with an MCP Client
- Developing an MCP Server
- Integrating databases and tools for the MCP Server
- Understanding HTTP and SSE (Server-Sent Events) transport
- Exposing prompts and resources
8. Operational monitoring
- Enabling actuator metrics
- Monitoring vector store operations
- Observing model interactions
- Tracking token counts
- Aggregating data in Prometheus and building dashboards
- Tracing AI operations
9. Security in generative AI
- Regulating document access via RAG
- Securing AI tools
- Mitigating adversarial prompting
- Moderating user input
10. Common generative patterns
- Summarizing content
- Translating messages
- Analyzing sentiment
11. The role of Agents
- Defining what an agent is
- Building agentic workflows
- Chaining prompts, routing tasks, and parallelization
- Accessing agents via MCP
Requirements
To get the most out of this course, participants should bring the following background:
- Strong proficiency in Java programming
- Hands-on experience with Spring and Spring Boot
- Confidence in building and configuring Spring Boot applications
- Fundamental knowledge of REST APIs and HTTP
- Basic understanding of JSON and application configuration
- A foundational grasp of generative AI and Large Language Models (LLMs)
- Recommended familiarity with databases and data access principles
- No prior exposure to Spring AI, RAG, MCP, or AI agents is necessary
Testimonials (1)
Detailed information provided on the more advanced topics requested.
