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LLM data extraction

LLM data extraction is the use of a large language model to read content and pull out specific fields by meaning, rather than by fixed rules like CSS selectors, returning structured values from unstructured text.

LLM data extraction uses a large language model to read content and produce structured fields from it. Because the model understands language and context, it can find a price, an author, or a publish date even when the wording or layout varies from one page to the next. This is fundamentally different from rule-based scraping, which depends on exact positions in the markup.

The risk with LLMs is that, left unconstrained, they can guess or hallucinate values that are not actually present. The way to control this is to combine the model with a strict contract and low randomness, and then to validate everything it returns.

Extracto does exactly that. It renders the page in a headless browser, passes the page text and metadata to an LLM with your JSON Schema as a strict contract at temperature zero, and validates the output against your schema before returning it. Missing fields come back as null, never a guessed value, so you get the flexibility of an LLM with the predictability of a typed, validated result.

Examples

Reading varied layouts

Two blog posts may present the author name in completely different places and formats. An LLM reads both by meaning and returns the author field consistently, where a single CSS selector would only fit one of them.

Honest nulls

If an article has no visible publish date, Extracto returns null for that field instead of inventing one, because the schema is validated and temperature zero discourages the model from filling gaps with plausible guesses.

See also

LLM data extraction: FAQ

How does LLM data extraction avoid hallucinating values?
Extracto constrains the model with your JSON Schema as a strict contract, runs it at temperature zero to minimize randomness, and validates every response. Fields that are not present on the page come back as null rather than as a guessed or invented value.
Why use an LLM instead of CSS selectors for extraction?
An LLM reads content by meaning, so it handles varied wording and layouts and keeps working when a site is redesigned. Selectors point at fixed positions in the markup and break whenever that structure changes, which creates constant maintenance.

Want to put this into practice? Extracto extracts structured data from a URL using a JSON schema, with the result validated before it leaves the API. Try the live demo or read the docs.