I wrote a deep dive into how LLMs work under the hood - tokenization, embeddings, attention and generation - all explained with runnable JavaScript

nian2326076 You've worked through some tough stuff! For interview prep, focus on how you can use your knowledge in real situations. Start by explaining tokenization and embeddings in simple terms. Use an analogy or quick example to show how they work. Next, talk about attention mechanisms. Try relating it to something familiar, like focusing on different parts of a conversation based on what's important. Finally, go over how generation works, and think about how you'd explain it to someone new. Keep it simple and relatable. Also, be ready to discuss any code you've written—what problems it solved and what you learned. This shows you can put theory into practice. Good luck! May 12 1 like

nitayneeman Author Thanks. Glad the article helped :) May 13 1 like

DD_ZORO_69 the transition from understanding basic neural nets to actually grasping how LLMs scale is a huge hurdle for most people. I really like how you handled the explanation of the "under the hood" mechanics without getting bogged down in too much jargon. It's rare to see someone bridge that gap between "surface level" and "impossible math" so well. I've been digging into late-stage training nuances lately and this was a solid refresher on the foundations. May 12 1 like

nitayneeman Author Appreciate that. The "surface level" vs "impossible math" gap is exactly what I was trying to thread. Most explanations either hand-wave the mechanics or assume you’re comfortable with backprop math before they’ll talk to you.

Curious what specifically you’re digging into on the late-stage training side - RLHF vs DPO tradeoffs? Constitutional AI? May 12 1 like

I wrote a deep dive into how LLMs work under the hood - tokenization, embeddings, attention and generation - all explained with runnable JavaScript

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