Spring AI Eğitimi

1. Getting Started with Spring AI

• Initializing projects and setup
• The function of prompts and their submission process
• Creating your first test case
• Selecting the appropriate model
• Configuring model parameters
• Overview of Spring AI features

2. Analyzing Responses

• Verifying the relevance of outputs
• Evaluating runtime accuracy

3. Prompt Engineering Deep Dive

• Leveraging prompt templates
• Creating custom prompt templates
• Grasping context management
• Understanding roles and their significance
• Adjusting output generation via options
• Managing streaming and output formatting
• Interpreting response metadata

4. Leveraging Data and Documents

• Comprehending RAG (Retrieval-Augmented Generation)
• Configuring vector stores and ingesting documents
• Implementing a basic RAG solution
• Utilizing advisors for RAG
• Exploring modular RAG functionalities

5. Memory in AI Systems

• The necessity of memory
• Integrating and setting up memory for conversation support
• Managing conversation IDs
• Implementing persistent memory
• Storing chat history in vector stores

6. AI Tools

• Enabling tool capabilities in applications
• Understanding tool functionalities
• Developing and implementing tools
• Using functions as tools

7. The Model Context Protocol (MCP)

• The need for MCP
• Interacting with an MCP Client
• Building an MCP Server
• Integrating databases and tools for the MCP Server
• Comprehending HTTP and SSE (Server-Sent Events) transport
• Exposing prompts and resources

8. Operational Monitoring

• Activating actuator metrics
• Monitoring vector store activities
• Tracking model interactions
• Tracking token usage
• Aggregating data in Prometheus and building dashboards
• Tracing AI operations

9. Generative AI Safeguards

• Restricting document access via RAG
• Protecting tools
• Mitigating adversarial prompting
• Moderating user inputs

10. Standard Generative Patterns

• Summarizing content
• Translating messages
• Performing sentiment analysis

11. The Role of Agents

• Defining an agent
• Building agentic workflows
• Chaining prompts, routing tasks, and parallelization
• Accessing agents via MCP