What Is Structured Data, and Why LLMs Prefer It
Everything your business knows about itself exists in one of two forms. Either it is written for people, in sentences that need interpreting, or it is written for machines, in fields that need no interpreting at all. The second form is structured data. It is not a new idea. It is the difference between a paragraph and a form, and it is the single biggest factor in whether an AI system gets your facts right.
Unstructured and structured, side by side
Unstructured: "We are delighted to welcome guests to our rooftop pool, open from early morning until late evening, with towels and loungers provided and a small bar serving drinks from midday."
Structured: Amenity, Rooftop Pool. Opens, 07:00. Closes, 21:00. Towels provided, yes. Bar, yes. Bar opens, 12:00.
A person enjoys the first and finds the second dry. A machine finds the first ambiguous and the second exact. "Early morning" could be six or eight. "Late evening" could be nine or eleven. "A small bar serving drinks" might or might not mean alcohol. The structured version answers every question before it is asked.
Why language models prefer it
Language models are prediction engines. They produce the most likely next word given what is in front of them. When the text in front of them is clear and explicit, the most likely continuation is correct. When it is vague, the most likely continuation is plausible, and plausible is not the same as true.
Structured data removes the vagueness. There is no inference step between "Closes, 21:00" and the answer "the pool closes at nine". The model is reading, not guessing.
It also fits how retrieval works. Retrieval systems cut content into chunks and fetch the ones most relevant to a question. A structured record is already a clean, self contained chunk: one entity, one set of labelled facts. A long paragraph has to be cut somewhere arbitrary, and the cut often separates a fact from the context that made it meaningful.
And it survives aggregation. When an AI combines facts from many sources about many businesses, structured fields line up. Hours compare to hours, prices to prices. Prose from one site cannot be compared to prose from another without a lot of interpretation, and interpretation is where errors creep in.
Structured does not mean technical
A spreadsheet is structured data. A menu with items, descriptions and prices in consistent columns is structured data. An FAQ page where each question has one clear answer is close to structured. The structure is about consistency and labelling, not about code.
Code comes in when you want machines to read the structure directly, which is where formats like JSON-LD and vocabularies like Schema.org fit. But the hard part is upstream: deciding what the facts are, giving each one a label, and keeping them consistent across everywhere they appear.
What this means in practice
Start with the facts people actually ask about. Hours, location, prices, policies, amenities, what each outlet serves. Write each as a labelled statement rather than burying it in prose.
Keep prose for persuasion, structure for facts. The welcoming paragraph can stay. The hours should also exist in a form that cannot be misread.
Use one version of each fact. If the hours appear in three places, they should be identical in all three. Machines treat disagreement as uncertainty.
Mark it up where it lives. Once facts are structured, publishing them in machine readable form on the relevant page is a small step with a large effect.
The takeaway
Structured data is facts written so that nothing has to be inferred. Language models are inference machines, and the less they have to infer about you, the more often they are right. Giving them the structured version is the simplest way to be understood correctly.