How LLMs Generate Text — one token at a time

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<p>An LLM doesn't "know" that Paris is the capital of France the way a database row knows it. It doesn't understand your question, and it isn't reasoning before it answers.</p>

<p>In this blog post, we will see how an LLM request and response cycle works, from the second you hit send to the moment the last word lands on your screen. I will not throw a

<h1> How Many R's in Strawberry? Your AI Has No Idea Why That's Hard </h1> <p>How many R's are in the word "strawberry"?</p> <p>If you ask ChatGPT, there's a decent chance you'll

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<p>Most "multimodal" AI products today are really a relay team. One model plans, then hands the job to an image model, a video model, a detector, or a robot policy. Reka's new

<!-- SC_OFF --><div class="md"><p>Every &quot;AI agent&quot; that controls a computer today does this: generate text → parse it → execute → serialize result back to text → repeat.

<blockquote> <p><strong>TL;DR</strong> — A glossary to actually understand the terms you hit when reading about LLMs: token, embedding, attention, KV cache, GQA, MoE, quantization