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Duration 21 hours
Course Outline
1. Introduction to Spring AI
- Project setup and configuration
- The significance of prompts and prompt submission
- Writing your first test
- Selecting an appropriate model
- Configuring model parameters
- An overview of Spring AI features
2. Interpreting responses
- Verifying the relevance of answers
- Assessing runtime accuracy
3. Deep dive into prompts
- Utilizing prompt templates
- Creating new prompt templates
- Grasping contextual nuances
- Understanding the role and its impact
- Influencing generation via configuration options
- Streaming and formatting output data
- Analyzing response metadata
4. Leveraging proprietary data and documents
- Comprehending RAG (Retrieval-Augmented Generation)
- Initializing vector stores and ingesting documents
- Implementing a basic RAG solution
- Building RAG using advisors
- Exploring modular RAG features
5. The significance of memory in AI
- The necessity of memory in AI systems
- Implementing and configuring memory for conversations
- Managing conversation IDs
- Enabling persistent memory support
- Storing chat history in vector stores
6. AI Tools
- Developing tool-enabled applications
- Understanding tool capabilities
- Creating and deploying tools
- Using functions as tools
7. The Model Context Protocol (MCP)
- The need for MCP
- Interacting with an MCP Client
- Developing an MCP Server
- Integrating databases and tools for the MCP Server
- Understanding HTTP and SSE (Server-Sent Events) transport mechanisms
- Publishing prompts and resources
8. Operational monitoring
- Activating actuator metrics
- Monitoring vector store operations
- Tracking model interactions
- Counting tokens
- Integrating with Prometheus and building dashboards
- Tracing AI-specific operations
9. Safeguards 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 an agent
- Building agentic workflows
- Chaining prompts, routing tasks, and parallelizing processes
- Accessing agents via MCP
Requirements
To successfully engage with this course, participants should possess:
- Proficiency in Java programming
- Practical experience working with Spring and Spring Boot
- Knowledge of building and configuring Spring Boot applications
- A fundamental grasp of REST APIs and HTTP
- A basic understanding of JSON and application configuration
- Essential concepts of generative AI and Large Language Models (LLMs)
- Recommended familiarity with databases and data access principles
- No prior experience with Spring AI, RAG, MCP, or AI agents is necessary
Testimonials (1)
Detailed information provided on the more advanced topics requested.
