"Hello There" My name is Chris. I'm 53 as I write this in October of 2025, and I'm a gamer, a golfer, and a guy who's been (and continues to be) on a serious health journey. After losing and then gaining over 190 pounds and facing significant cardiac events, I thought I was doing everything right by following a 'keto' diet. I was wrong. I discovered I was eating 'dirty keto'—my 'health foods' were full of inflammatory oils, hidden starches, and artificial sweeteners that were working against me. 'The Path is Too Deep' is my personal blog about ditching the marketing and discovering the power of a Clean, Anti-Inflammatory, Whole-Food Ketogenic Lifestyle. I'll be sharing what I've learned about reading labels, my ongoing journey with weight loss, my strategies for managing mental health (ADHD/dysthymia), and my thoughts on gaming, golf, and technology. It's my personal rulebook for taking back control. "Not all those...
When you interact with a Large Language Model (LLM) like Gemini or ChatGPT, the system generates responses that feel remarkably human. It is easy to anthropomorphize this interaction and assume the machine is "thinking" or "understanding" the prompt. Biologically and mechanically, this is entirely false. An LLM does not possess cognition, reasoning, or awareness. It is a highly complex, probabilistic math engine. Here is the mechanical architecture of how an LLM processes your inputs, broken down into its three foundational components: tokens, context windows, and next-word prediction. The Token: The Atomic Unit of Data An LLM does not read English words. It reads numbers. Before a model can process your prompt, the text must be translated into a mathematical format through a process called tokenization. A "token" is a fragment of text. It is not necessarily a whole word; it is often a syllable or a cluster of letters. Short words: Common words (like ...