How LLMs Actually Work (No Math): Tokens, Attention & Sampling

Описание: You don't need a PhD to understand how large language models work, but every AI engineering interview probes whether you do. Module 2 of the Belay AI Engineer track opens the black box, with intuition instead of math.
In this module you'll understand:
• Tokens: the atoms the model actually reads, and why they drive your bill and the weird bugs
• Attention: how each token figures out which other tokens matter (query, key, value, made visual)
• Sampling: what temperature and top-p really do to the model's next-word choice
• Context & the KV cache: what fills the window, and why prompt caching is free money
• How models are trained, and why hallucination is structural, not a bug
• Reasoning models: what "thinking" changed, and when the extra cost is worth it

This is the video version of the Belay bootcamp. Follow the full interactive track, with runnable code, an AI tutor, and a visual skill tree, at https://takebelay.com

⏱ Chapters
00:00 Introduction
00:45 Tokens: The Atoms of Everything
04:05 Attention, in Plain English
06:54 Sampling: Temperature & Top-p
10:02 Context, the KV Cache & Prompt Caching
13:08 How Models Are Made
15:53 Reasoning Models & the 2026 Landscape

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Full track:    • Become an AI Engineer — Full Track (2026)  
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Narration is AI-generated; the content is human-authored and reviewed. This video is for education only.

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