Fundamentals of LLMs

Large Language Models like GPT-4 may feel intelligent—but how do they actually “think”? Spoiler: it’s not magic. It’s math, probabilities, and some clever mechanisms under the hood.

From chain-of-thought prompting to temperature and top-k sampling, LLMs use a blend of techniques to generate coherent, context-aware responses. These aren’t just technical knobs—they shape everything from creativity to accuracy in the text you see.

In this post, I’ll break down how these systems work behind the scenes. Whether you’re building with LLMs or just curious how they “decide” what to say next, this guide will demystify the process—one prompt at a time.

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