From Words to Vectors: How LLMs Actually Understand and Generate Language

What's it about?

After our last event, the feedback was clear: people want to learn more about LLMs — and how to bring them into their own workflows. But before we can use them well, we need a real feel for how they work under the hood. So this session goes back to the roots. A machine doesn't see words — it sees numbers. We'll follow that whole journey in plain English: * **Encoding** — how raw language gets turned into numbers a model can work with (tokens and embeddings), and why that first step shapes every