Structured extraction is the AI workflow that turns unstructured text — an invoice, a contract clause, an email thread — into a typed object that matches a strict schema. The model's output is no longer prose for a human to read; it's a record the next system in the pipeline can consume deterministically.
Extraction vs. generation.
Generation produces free text. Extraction produces a typed payload. The same base model handles both, but the prompt, the schema, and the validation layer are different. An extraction pipeline that drifts back into generation is the most common cause of structured-output regression — the model starts adding a polite preamble, the JSON parser fails, the downstream system breaks silently.
The extraction stack.
- Schema definition. A typed contract (TypeScript interface, Zod schema, JSON Schema). Required vs. optional fields are explicit; field types are tight.
- Prompted extraction. The model is given the source text, the schema, and a small number of canonical examples. Output goes through structured-output constraints (JSON mode, function-call schema).
- Validation. The output is parsed against the schema. Validation failures trigger a single retry, then fail closed.
- Confidence scoring. For each extracted field, a confidence (model-reported or judge-graded). Low-confidence extractions route to human review instead of straight to production.
Versus document intelligence.
Structured extraction is the inner primitive; document intelligence is the pipeline that wraps it (ingest, classify, extract, validate, route). You can have structured extraction without document intelligence (a single endpoint that turns one email into one ticket), but you can't have document intelligence without structured extraction at its core.
Frequently asked.
- What is structured extraction?
- Structured extraction is the AI workflow that turns unstructured text into a typed object that matches a strict schema. Instead of free-form prose, the model produces a typed payload (JSON, function-call args) that the next system in the pipeline can consume deterministically.
- How is structured extraction different from prompt engineering?
- Prompt engineering is the general discipline of shaping model instructions. Structured extraction is the specific application where the output must conform to a schema. The prompt still matters, but the schema and the validation layer carry equal weight — the strictest schema is the most reliable extractor.
- What happens when extraction fails?
- Schema validation catches malformed outputs and triggers a single retry with a stricter instruction. If the retry also fails, the system fails closed — the downstream pipeline is not given a half-correct payload. Low-confidence successful extractions route to human review rather than straight to production.
- When do we need structured extraction over a regular LLM call?
- Whenever the output has to be consumed by another system rather than read by a human. CRM record creation, invoice processing, contract field extraction, ticket triage, anything where a downstream step depends on typed fields rather than prose.