Schema-driven extraction

What Schema-driven extraction means in practice

Schema-driven extraction inverts the classical extraction approach. Instead of training a model to identify all possible fields, the model is given the target schema at inference time and asked to populate it. This pattern works because modern LLMs are good at structured output (especially with function-calling or JSON mode) and because the schema acts as a strong prior on what to look for. The operational payoff is iteration speed: adding a new field to an extraction workflow is a schema edit, not a model retrain. The trade-off is per-document cost (an LLM call per document is more expensive than running a trained extractor), which is why production deployments often run a cheap classifier first to route only the long-tail documents to the LLM path.