A large language model (LLM) is an AI system trained on vast amounts of text to predict what words come next. From that single skill — guessing the most likely continuation — comes its apparent ability to answer questions, summarise documents, translate, and write.
How it works
An LLM learns statistical patterns across billions of sentences. When you give it a prompt, it doesn't look anything up; it generates a response token by token, each choice shaped by the patterns it absorbed during training. There is no database of facts inside it — only a compressed sense of how language tends to flow.
This is why an LLM can be so useful and so unreliable at once. Predicting fluent text is not the same as stating true text. A model will produce a confident, well-formed sentence whether or not the underlying claim is correct, because correctness was never what it optimised for. When that gap shows — a plausible answer with no basis in reality — it's called an AI hallucination.
Why grounding matters
On its own, an LLM answers from memory: a blur of everything it once read, with no way to point at a source. You can't check its work, because there's nothing to check it against.
Grounding closes that gap. Instead of asking the model to recall, you retrieve the relevant passages from a real document and place them in front of it — the approach known as retrieval-augmented generation. The model then writes from text you can see, and source-grounding keeps each statement tied to the passage it came from. The output stops being a guess and becomes something verifiable.
This is the whole shape of how Sidenote works: it reads your document, retrieves the passages that matter, and grounds every answer in them — so each claim carries a citation you can click straight back to the source. See it in action on the Citations feature page.