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.

• 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

• 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.

• (00:00:00) - ChatGPT Curious
• (00:00:38) - What Is ChatGPT?
• (00:02:47) - ChatGPT
• (00:04:32) - parameters of OpenAI's LLMs
• (00:06:11) - Laplike 2.8
• (00:13:31) - ChatGPT and the Probability of Outputs
• (00:19:22) - ChatGPT: Algorithms That Predict the Next Word
• (00:21:39) - ChatGPT: Probabilistic Models (
• (00:28:28) - ChatGPT: What Can ChatGPT Do?
• (00:31:00) - What to Watch Out For With ChatGPT
• (00:36:13) - A Brief History of Chat GPT