LLM API Foundations: Let's Build a Basic Agent
Hosted by Jay Wengrow
In this video
What you'll learn
Build an LLM-powered agent from scratch
You'll build a mini agent with nothing more than raw Python and an LLM API.
Equip an LLM with tools
Although LLMs do nothing more than generate text, you'll equip a model with tools that act upon the real world.
Gain deeper understanding of agents
By building your own agent from scratch, you'll understand how agents really work under the hood.
Why this topic matters
The next step in AI engineering after building a chatbot is to build an agent, an LLM-powered app that acts upon the real world. Although, under the hood, LLMs do nothing more than generate text, they can be given the power to trigger real code functions. These "tools" can do almost anything, unlocking an incredible amount of functionality for your LLM app.
You'll learn from
Jay Wengrow
CEO of Actualize, Author of A Common-Sense Guide to AI Engineering
Jay Wengrow is an experienced educator and software engineer, and the author of A Common-Sense Guide to AI Engineering. He is also the founder of Actualize, a software and AI engineering education company, and specializes in making advanced technical topics approachable for professionals across industries. He also wrote the popular Common-Sense Guide to Data Structures and Algorithms book series.
Go deeper with a course
Let's Code LLM Chatbots and Agents from Scratch
Jay Wengrow
Software Engineer and Educator, Author of A Common-Sense Guide to AI Engineering