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

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 everything after it.
• Representation — how models learn to understand a sequence: from early recurrent networks, LSTMs, and GRUs that tried to "remember" context, to where they hit their limits.
• Generation — the breakthrough that changed everything: attention and the Transformer, and how that one idea opened the door to the GPTs and LLMs we all rely on today.

The goal is intuition first — enough under-the-hood detail to make the "oh, that's why it works" moments click, with no deep learning background required. Just curiosity.

Once the foundation is down, future sessions get to the fun part: actually putting these models to work.

Got a question about AI you'd like us to tackle? Drop it in the comments — we'd love to fold it into the discussion.