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Course Outline
1. Introduction to Spring AI
- Setting up projects and configuration
- The function of prompts and submission processes
- Writing initial tests
- Selecting the appropriate model
- Configuring model parameters
- An overview of Spring AI capabilities
2. Analyzing responses
- Verifying the relevance of answers
- Evaluating accuracy during runtime
3. Prompting in depth
- Utilizing prompt templates
- Creating custom prompt templates
- Contextual understanding
- The significance of the role field
- Steering response generation via options
- Streaming and output formatting
- Interpreting response metadata
4. Leveraging your data and documents
- Concepts behind RAG (Retrieval-Augmented Generation)
- Configuring vector stores and ingesting documents
- Implementing basic RAG workflows
- Building RAG solutions using advisors
- Exploring modular RAG features
5. The impact of memory in AI
- The necessity of memory in AI systems
- Implementing and configuring memory for conversations
- Managing conversation IDs
- Enabling persistent memory
- Persisting chat history in vector stores
6. AI Tools
- Enabling tool capabilities in applications
- Understanding the scope of AI tools
- Developing and integrating tools
- Using functions as tools
7. The Model Context Protocol (MCP)
- Why MCP is essential
- Interacting with MCP Clients
- Developing MCP Servers
- Integrating databases and tools for MCP Servers
- Understanding HTTP and SSE (Server-Sent Events) transport
- Exposing prompts and resources
8. Operational monitoring
- Activating actuator metrics
- Monitoring vector store activity
- Observing model interactions
- Tracking token usage
- Visualizing data in Prometheus dashboards
- Tracing AI-specific operations
9. Security in generative AI
- Controlling document access via RAG
- Securing AI tools
- Mitigating adversarial prompting
- Moderating user inputs
10. Standard generative patterns
- Summarizing content
- Translating messages
- Analyzing sentiment
11. The function of Agents
- Defining AI agents
- Building agentic workflows
- Chaining prompts, task routing, and parallelization
- Accessing agents via MCP
Requirements
To get the most out of this course, participants should possess:
- Solid proficiency in Java programming
- Practical experience working with Spring and Spring Boot
- Comfort in building and configuring Spring Boot applications
- A foundational grasp of REST APIs and HTTP
- Basic knowledge of JSON and application configuration
- Fundamental understanding of generative AI and Large Language Models (LLMs)
- Recommended familiarity with databases and data access concepts
- No prior experience with Spring AI, RAG, MCP, or AI agents is necessary
21 Hours
Testimonials (1)
Detailed information provided on the more advanced topics requested.
