Welcome to the Build Multi-Agent LLM Applications with AutoGen!

Are you excited about exploring the world of Generative AI? In this course, we’ll learn how to create conversable and customizable AI agents powered by Large Language Models. This is a hands-on course with exercises in Python. We’ll cover how to integrate external tools like APIs and web scrapers with agents. We’ll cover advanced techniques like Retrieval Augmented Generation, Prompt Engineering (ReAct), and Task Decomposition. We’ll also implement different conversational patterns like group chats and nested chats.

Intended Audience:

This intermediate-level course is designed for data scientists, machine learning engineers, and software engineers aiming to expand their expertise into the LLM/Generative AI space.

Course Outline:

• Environment Setup

• Getting Started with AutoGen (Basic Concepts)

• Large Language Model Agents

• Agents with Human-in-the-Loop

• Agents with Code Execution Capability

• Agents with access to external tools like APIs and web scrapers

• Agents in different Conversational Patterns (Sequential, Group, Nested Chats)

• Agents with GPT-4 Turbto/DALL-E Image Generation Endpoints

• Prompt Engineering Techniques (ReAct) with Agents

• Retrieval Augmented Generation (RAG) using Chroma DB and LLM Agents

• Task Decomposition (Build Automated LLM Agents)

• Message Transformations for LLM Agents

• Using Non-OpenAI/Open Source Models with LM Studio

Join me on this journey to explore the world of LLM Agents and Generative AI!

Who this course is for:

• Data Scientists and Machine Learning Engineers who’d like to integrate LLMs in various use-cases
• Software Engineers who need a hands-on guide to develop LLM-based multi-agent workflows
• Architects who need a high-level understanding of what’s possible with agentic workflows