In this episode of ChatGPT Curious, I lay the groundwork for understanding what ChatGPT actually is, without getting too lost in the weeds. I break down key terms like LLM and parameters, explain how the model was trained (hint: lots of math), why it sometimes spits out wrong info, and what all of this means for how you use it. I also touch on the environmental cost, what the free vs. paid versions can actually do, and how to think critically about its outputs. If you've ever felt a little confused, a little curious, or both, this one's for you. Main Topics Covered Why it’s worth understanding the foundation of ChatGPT What is ChatGPT? Brief history of OpenAI and the development of GPT What GPT actually stands for and what changed with each version What “parameters” are and how they shape the model’s responses How language is turned into numbers via tokens What happens during training Human involvement in model training via RLHF (reinforcement learning with human feedback) Probabilistic vs deterministic systems and what that means for output accuracy The environmental cost of “compute” and an analogy for mindful use What the free version can do (and can’t), including search, uploads, and voice What the paid version offers What ChatGPT is not What to watch out for Real-life use case: Trying to fix a bike derailleur using ChatGPT Links & Resources for This Episode Try the Tokenizer Try out the Free version of ChatGPT Subscribe to the ChatGPT Curious Newsletter Submit a Question Visit the Website Feeling curious AND generous?  Click here to support the podcast.