Everything You Wanted To Know About LLMs, but Were Too Afraid To Ask with Matthew Lynley, Founding Writer of Supervised

With the rise of GenAI, LLMs are now accessible to everyone. They start with a very easy learning curve that grows more complicated the deeper you go. But, not all models are created equal. It’s critical to design effective prompts so users stay focused and have context that will drive how productive the model is. In this episode, Matthew Lynley, Founding Writer of Supervised, delivers a crash course on LLMs. From the basics of what they are, to vector databases, to trends in the market, you’ll learn everything about LLMs that you’ve always wanted to know. Matthew has spent the last decade reporting on the tech industry at publications like Business Insider, The Wall Street Journal, BuzzFeed News, and TechCrunch. He founded the AI newsletter, Supervised, with the goal of helping readers understand the implications of new technologies and the team building it. Satyen and Matt discuss the inspiration behind Supervised, LLMs, and the rivalry between Databricks and Snowflake.

Om Podcasten

Some people can see things that nobody else can. They seem to be able to peer around corners and into the future. These seemingly super powers come from being able to synthesize the data all around us. They approach problems with a curious and rational mind. They think differently and encourage others to embrace data culture. We call them “data radicals” because they transform themselves and the world around them In this podcast, we talk to these Data Radicals to understand what makes their approach so unique and how it can be replicated.