What a Neural Network Actually Computes: From Token IDs to Matrix Multiplication

<h1> How LLMs Work: A Journey Through Tokens, Attention, and Transformers </h1> <p>“My favourite rock band is…”</p> <p>How does a large language model take that unfinished

<p>Everything starts from something you already know:</p> <p>y = mx + c</p> <p>That's just a line. But stack enough of them, connect them, and add non-linearity? You have a neural

<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

<p>In the <a href="https://dev.to/rijultp/understanding-seq2seq-neural-networks-part-8-when-does-the-decoder-stop-4jdi">previous articles</a>, we understood Seq2Seq models. Now,

<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

<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>