Everyone's using LLMs. Few people truly understand what's happening under the hood.

If you're an engineer integrating GPT-5 into your product, a PM scoping an AI feature, or a founder trying to decide between fine-tuning and prompting — you need more than surface-level intuition. You need to understand the machinery that makes these models tick.

This article is that walkthrough. We'll trace the full lifecycle of a large language model: from raw internet text to a system that can write poetry, debug code, and (occasionally) hallucinate with alarming confidence.

No PhD required. Just curiosity and a tolerance for big numbers.