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Course Outline
1. Introduction to Spring AI
- Creating and configuring projects
- The function of prompts and prompt submission
- Authoring the first test
- Selecting a model
- Configuring the model
- An overview of Spring AI features
2. Analyzing responses
- Verifying the relevance of answers
- Ensuring accuracy during runtime
3. Prompts in detail
- Utilizing prompt templates
- Creating a new prompt template
- Comprehending context
- Understanding the role and its significance
- Guiding response generation through options
- Streaming and formatting output
- Examining response metadata
4. Utilizing your data and documents
- Comprehending RAG (Retrieval-Augmented Generation)
- Configuring the vector store and ingesting documents
- Implementing a basic RAG solution
- Implementing RAG with an advisor
- Modular RAG features
5. The role of memory in AI
- The necessity of memory
- Implementing and configuring memory for conversations
- Managing conversation IDs
- Enabling persistent memory
- Storing chat history in a vector store
6. AI Tools
- Enabling tools in an application
- Understanding tool capabilities
- Developing and deploying tools
- Using functions as tools
7. The Model Context Protocol (MCP)
- The purpose of 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 operations
- Analyzing model interactions
- Token counting
- Implementing Prometheus and building dashboards
- Tracing AI operations
9. Safeguards in generative AI
- Managing document access via RAG
- Securing tools
- Mitigating adversarial prompting
- Moderating user input
10. Standard generative patterns
- Content summarization
- Message translation
- Sentiment analysis
11. The role of Agents
- Defining an agent
- Developing agentic workflows
- Chaining prompts, task routing, and parallelization
- Accessing agents via MCP
Requirements
Learners are expected to have the following background:
- A solid grasp of Java programming
- Practical experience with Spring and Spring Boot
- Familiarity with the setup and configuration of 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 databases and data access concepts is advised
- 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.
