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Duration 21 hours
Course Outline
1. Introduction to Spring AI
- Creating and configuring projects
- The function of prompts and their submission process
- Developing initial tests
- Selecting the appropriate model
- Configuring model settings
- An overview of Spring AI capabilities
2. Interpreting responses
- Verifying the relevance of answers
- Evaluating runtime accuracy
3. Detailed exploration of prompts
- Utilizing prompt templates
- Crafting custom prompt templates
- Comprehending context
- The significance and role of context
- Modifying response generation via options
- Streaming and formatting output data
- Analyzing response metadata
4. Leveraging proprietary data and documents
- Concepts of RAG (Retrieval-Augmented Generation)
- Configuring vector stores and ingesting documents
- Implementing a basic RAG solution
- Employing RAG with an advisor pattern
- Exploring modular RAG features
5. The function of memory in AI
- The necessity of memory systems
- Integrating and configuring memory for conversations
- Managing conversation IDs
- Implementing persistent memory support
- Storing chat memory within vector stores
6. AI Tools
- Enabling tools in applications
- Understanding tool capabilities
- Developing and deploying tools
- Using functions as tools
7. The Model Context Protocol (MCP)
- The rationale for using MCP
- Interacting with an MCP Client
- Developing an MCP Server
- Integrating databases and tools into the MCP Server
- Understanding HTTP and SSE (Server-Sent Events) transports
- Exposing prompts and resources
8. Operational monitoring
- Activating actuator metrics
- Monitoring vector store activities
- Tracking model interactions
- Counting tokens
- Integrating with Prometheus and building dashboards
- Tracing AI operations
9. Safeguarding generative AI
- Controlling document access via RAG
- Securing AI tools
- Mitigating adversarial prompting
- Moderating user inputs
10. Common generative AI patterns
- Summarizing content
- Translating messages
- Analyzing sentiment
11. The role of Agents
- Defining an agent
- Building agentic workflows
- Chaining prompts, task routing, and parallel processing
- Accessing agents via MCP
Requirements
Participants are expected to have:
- A solid grasp of Java programming
- Hands-on experience with Spring and Spring Boot
- Proficiency in building and configuring Spring Boot applications
- A foundational understanding of REST APIs and HTTP
- A basic comprehension of JSON and application configuration
- A fundamental understanding of generative AI and Large Language Models (LLMs)
- Knowledge of database and data access concepts is recommended
- No prior expertise in Spring AI, RAG, MCP, or AI agents is necessary
Testimonials (1)
Detailed information provided on the more advanced topics requested.
