Prompt is the full input you hand to a language model — everything the model sees before it generates a single word of output. In the simplest case that is a one-line question; in practice, a prompt usually bundles an instruction, any retrieved source passages, and the user's question together into a single piece of text.
Why it matters
A model can only work with what it is given. The quality, structure, and contents of a prompt directly determine the quality of the output: an ambiguous instruction produces a vague answer; a precise one, grounded in real source text, produces something you can actually trust and check.
This is especially important in document AI. When Sidenote answers a question about a PDF or article, it does not leave the model to draw on its own memory. It retrieves the most relevant passages from the document and places them directly in the prompt alongside your question, so the model is writing from text you can read, not text it once absorbed from somewhere on the web.
The way that prompt is constructed — which passages are chosen, how much context surrounds them, what the model is asked to do with them — is what separates a grounded, citable answer from a confident hallucination.
Prompts can also be shaped by example rather than instruction alone. Few-shot prompting places a handful of worked examples in the prompt to steer the model's format or behaviour, without any retraining. At the other end of the risk spectrum, prompt injection describes attempts to hijack a model by smuggling adversarial instructions into the prompt through the very documents it is reading.