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Duration 21 hours
Course Outline
1. Introduction to Spring AI
- Initiating project setup and configuration
- Exploring the function of prompts and their submission
- Creating initial test cases
- Selecting an appropriate model
- Configuring the selected model
- Overview of Spring AI features
2. Interpreting responses
- Verifying the relevance of generated answers
- Evaluating accuracy during runtime
3. Prompt engineering in depth
- Leveraging prompt templates
- Creating custom prompt templates
- Comprehending context
- Understanding the significance of roles
- Modifying response generation through options
- Managing streaming and output formatting
- Analyzing metadata within responses
4. Integrating proprietary data and documents
- Grasping the concept of RAG (Retrieval-Augmented Generation)
- Configuring the vector store and ingesting documents
- Implementing a basic RAG system
- Utilizing RAG with an advisor pattern
- Exploring modular RAG features
5. The importance of memory in AI
- The necessity of memory mechanisms
- Implementing and configuring memory for conversation support
- Managing conversation IDs
- Enabling persistent memory
- Storing chat history in a vector store
6. Leveraging AI Tools
- Building tool-enabled applications
- Understanding tool capabilities
- Developing and deploying tools
- Using functions as tools
7. The Model Context Protocol (MCP)
- The rationale behind MCP
- Utilizing an MCP Client
- Developing an MCP Server
- Integrating databases and tools for the MCP Server
- Comprehending HTTP and SSE (Server-Sent Events) transport
- Exposing prompts and resources
8. Operational monitoring
- Activating actuator metrics
- Reviewing vector store operations
- Analyzing model interactions
- Tracking token usage
- Integrating data into Prometheus and building dashboards
- Tracing AI operations
9. Security measures in generative AI
- Regulating document access via RAG
- Securing AI tools
- Mitigating adversarial prompting
- Moderating user inputs
10. Standard generative patterns
- Content summarization
- Message translation
- Sentiment analysis
11. The function of Agents
- Definition of an agent
- Building agentic workflows
- Chaining prompts, routing tasks, and parallelizing processes
- Accessing agents via MCP
Requirements
Prospective participants are expected to possess the following skills:
- Solid proficiency in Java programming
- Practical expertise with Spring and Spring Boot
- Experience in building and configuring Spring Boot applications
- Fundamental knowledge of REST APIs and HTTP
- Basic comprehension of JSON and application configuration
- Foundational understanding of generative AI and Large Language Models (LLMs)
- Familiarity with database and data access concepts is advised
- Prior experience with Spring AI, RAG, MCP or AI agents is not mandatory
Testimonials (1)
Detailed information provided on the more advanced topics requested.
