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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
- Developing the first test
- Selecting a model
- Configuring the model
- Overview of Spring AI capabilities
2. Interpreting responses
- Verifying the relevance of answers
- Assessing runtime accuracy
3. Prompt engineering in depth
- Utilizing prompt templates
- Creating custom prompt templates
- Comprehending context
- The significance of roles
- Guiding response generation via options
- Streaming and structuring output
- Response metadata
4. Leveraging personal data and documents
- Grasping RAG (Retrieval-Augmented Generation)
- Establishing a vector store and ingesting documents
- Implementing a basic RAG solution
- Deploying RAG with an advisor
- Modularizing RAG features
5. Memory's role in AI
- The necessity of memory
- Implementing and configuring memory for conversations
- Managing conversation IDs
- Enabling persistent memory
- Saving chat memory in a vector store
6. AI Tools
- Implementing tool-enabled applications
- Exploring tool functionalities
- Developing and deploying tools
- Utilizing functions as tools
7. Model Context Protocol (MCP)
- The need for MCP
- Interacting with an MCP Client
- Developing an MCP Server
- Database and tool integration for the MCP Server
- Understanding HTTP and SSE (Server-Sent Events) transport
- Exposing prompts and resources
8. Operational monitoring
- Activating actuator metrics
- Reviewing vector store activities
- Analyzing model interactions
- Token tracking
- Aggregating data in Prometheus and building dashboards
- Tracing AI operations
9. Security in generative AI
- Regulating document access via RAG
- Protecting tools
- Mitigating adversarial prompting
- Moderating user input
10. Standard generative patterns
- Summarizing content
- Translating messages
- Performing sentiment analysis
11. The function of Agents
- Defining an agent
- Building agentic workflows
- Chaining prompts, task routing, and parallelization
- Accessing agents via MCP
Requirements
Participants are expected to possess:
- A solid command of Java programming
- Hands-on experience with Spring and Spring Boot
- Proficiency in developing and setting up Spring Boot applications
- A foundational grasp of REST APIs and HTTP
- A basic understanding of JSON and application configuration
- A fundamental awareness of generative AI and Large Language Models (LLMs)
- Knowledge of databases and data access concepts is advisable
- No previous experience with Spring AI, RAG, MCP, or AI agents is necessary
Testimonials (1)
Detailed information provided on the more advanced topics requested.
