JSON Schema Extraction
Define the shape, get the data — extraction specified as a schema the model must fill.
JSON schema extraction is the pattern where extraction is specified as a schema the model must populate: the developer defines the output's shape — field names, types, required versus optional, enums, nested objects for line items and parties, descriptions that tell the model what each field means — and the extraction system returns a JSON instance conforming to it. The schema is simultaneously the requirement document, the model's instructions, and the validation contract, collapsing what template-era extraction spread across configuration, training, and post-processing into one declarative artifact.
The pattern's power comes from how modern models consume it. Vision-language and language models read the schema's field names and descriptions as instructions — "policy_expiry_date": "the date coverage ends, ISO 8601" steers the model to find, normalize, and format in one pass — which is why schema authoring is the new prompt engineering: precise descriptions, disambiguation of near-miss fields (invoice date versus due date versus delivery date), and enum values that match how documents actually phrase things measurably move accuracy. Enforcement closes the loop: structured-output modes and constrained decoding guarantee syntactic conformance (the JSON parses, types match), while semantic validation — the checksums, cross-field rules, and grounding checks this glossary treats elsewhere — guards against the conforming-but-wrong output that syntax enforcement cannot catch.
Operationally, schemas become versioned assets: evolved as requirements change (with migrations for downstream consumers, like any API), tested against benchmark documents per version (a reworded description is a model-behavior change), and organized into libraries per document type. The pattern's boundary is worth knowing: schemas excel where the target structure is known in advance; for exploratory processing of unknown documents, schema inference — having the model propose the schema from the documents — precedes it, and the two together form the arc from "what's in these files?" to production extraction.
Define what you want; let the model find it — extraction driven by target structure, not document template.
The whole point, arrived at — documents converted into the form every downstream system actually wants.
The model that writes the answer instead of pointing at it — power and peril in the same architecture.
Proof Perimeter runs document AI inside your own perimeter — with a provenance record on every field.
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