Do you know generative kung fu?

A primer on generative AI for everyone else

I’ve spent the past 3 weeks or so up to my ears in research on Generative AI and Large Language Models (LLMs) for a project. And, while this topic is not something that I thought I would be investing this much brain power into understanding, it’s helped me to contextualize all the hype around chatbots, AI-generated content creation, and the entire question of whether we should be using AI more in our day jobs. Maybe I’m just getting old and cranky.

Note: The definitions provided in this article are oversimplified at best, but they should give you the context you’ll need to hold a semi-intelligent conversation about the current state of generative AI.

The generative AI landscape is growing and evolving so quickly that even AI/ML engineers are having trouble keeping up with the latest and greatest tools. I’ve spent a healthy number of hours digging into everything from LinkedIn articles to Reddit forums to absorb as much knowledge as I can in a very short period of time - and chances are, it will all be outdated in a few short weeks. But, it’s also an exciting time for those who are energized by potential, unimagined changes that AI can bring to the world. New methodologies and formats and advances in technology and computing power are all emerging nearly every day. The current LLM market is like a massive hot dog-eating contest, but the contestants keep getting bigger and bigger instead of…well…exploding. Yet.

For those of us who do not spend every waking hour keeping up with the latest and greatest in the world of AI, here’s a very brief, salty primer on some of the terminology and concepts translated into human language that you’ll need to know in order to keep up with the kids these days.