Two Kinds of Knowing
The 'is this everything?' question lives on the border between what a model memorized and what it can look up. Here's how to read that border.
Over the past few months I have been watching how people interact with the newest AI tools, and it’s almost always the same reaction. Someone opens Claude’s Cowork, or Perplexity’s Computer, or Microsoft 365 Copilot, points it at a folder or an inbox, and watches it do the work. People are amazed. They should be. These things are genuinely impressive.
But look at what’s actually in front of you. None of them is a single exchange with a language model. Perplexity Computer takes one request and routes it across roughly twenty different models, spinning up sub-agents to handle the pieces. Cowork reaches into your files and keeps working in the background after you’ve closed your laptop. There’s a model somewhere inside each of them, wrapped in retrieval, tools, and orchestration. Calling one of these “an LLM” is like calling a car “an engine.”
Yet, however intricate the system gets, what comes back to you is still one smooth response. All that machinery collapses into prose that reads like one narrator who knows things.
So, before we get into how these systems work, and we will over the coming weeks, we have to cover something more fundamental. Knowledge itself. Specifically, the two kinds an LLM runs on, parametric and corpus.
Parametric knowledge, or what the model knows by heart. When a model is trained, it reads a staggering amount of text with a single job, predicting the next token. Tokens are chunks of text a few characters long, often a piece of a word rather than the whole word. Every time the model guesses, it nudges billions of internal dials, called parameters or weights, and over enough guesses the patterns in all that text get baked into those dials. There’s no library inside the model, no filing cabinet it flips through. The knowledge is smeared across the numbers, the way your ability to ride a bike is smeared across your muscles and nerves rather than written on an index card in your head. So when the model tells you water boils at 100°C, it isn’t looking that up. It’s remembering, the way you remember something you’ve read a thousand times.
