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Course Outline
1. Introduction to Spring AI
- Creating and configuring projects
- The function of prompts and submitting them
- Writing initial tests
- Selecting a model
- Configuring the model
- An overview of Spring AI features
2. Interpreting responses
- Verifying the relevance of answers
- Evaluating runtime accuracy
3. Deep dive into prompts
- Utilizing prompt templates
- Creating new prompt templates
- Comprehending context
- The role of context and its significance
- Influencing response generation via options
- Streaming and formatting output
- Response metadata
4. Leveraging your data and documents
- Understanding 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 importance of memory in AI
- The necessity of memory
- Implementing and configuring memory for conversations
- Managing conversation IDs
- Supporting persistent memory
- Storing chat memory in a vector store
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 MCP
- Utilizing an MCP Client
- Developing an MCP Server
- Integrating databases and tools for the MCP Server
- Understanding HTTP and SSE (Server-Sent Events) transport
- Exposing prompts and resources
8. Operational monitoring
- Activating actuator metrics
- Monitoring vector store operations
- Tracking model interactions
- Counting tokens
- Implementing Prometheus and building dashboards
- Tracing AI operations
9. Safety measures in generative AI
- Managing document access via RAG
- Securing tools
- Mitigating adversarial prompting
- Moderating user input
10. Common generative patterns
- Summarizing content
- Translating messages
- Analyzing sentiment
11. The function of Agents
- Defining an agent
- Building agentic workflows
- Chaining prompts, task routing, and parallelization
- Agent access through MCP
Requirements
Learners should possess:
- A solid grasp of Java programming
- Practical experience with Spring and Spring Boot
- Knowledge of building and configuring Spring Boot applications
- A basic understanding of REST APIs and HTTP
- A basic understanding of JSON and application configuration
- A basic understanding of generative AI and Large Language Models (LLMs)
- Familiarity with database 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.
