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Course Outline
1. Introduction to LLM Applications and AutoGen v0.4
- Overview of Large Language Models (LLMs): Understanding their capabilities and applications.
- Introduction to AutoGen v0.4: Exploring its features, architecture, and how it simplifies the development of agentic AI systems.
2. Core Concepts and Components of AutoGen
- Understanding the Layered Framework:
- Core Layer: Event-driven architecture supporting dynamic workflows.
- AgentChat API: Building task-driven agents with high-level APIs.
- Extensions: Integrating custom agents, tools, and memory modules for enhanced functionality.
- Asynchronous Messaging: Implementing event-driven and request-response interaction styles.
3. Building Your First Multi-Agent Application
- Defining Agents: Creating Assistant and User Proxy agents.
- Establishing Agent Communication: Setting up asynchronous messaging between agents.
- Implementing a Sample Application: Developing a simple multi-agent system to solve a specific task.
- Observability and Debugging Tools: Utilizing built-in metric tracking and message tracing for real-time monitoring.
4. Case Studies and Best Practices
- Real-World Applications: Examining successful implementations of AutoGen in various industries.
- Best Practices: Guidelines for designing efficient and scalable LLM applications using AutoGen.
- Challenges and Solutions: Addressing common challenges faced during development and their solutions.
- Q&A
The workshop is intended for:
- software developers
- data scientists
- data engineers
- people with programming background/inclination who want to learn about AI programming.
Requirements
. Prerequisites - Python programming
7 Hours
