LLM-Based Document Extraction

LLM-based document extraction is a paradigm shift from traditional template-driven or rule-based extraction. Rather than defining pixel coordinates or regex patterns for each field on each document type, you provide a large language model with the full document text and a natural-language description of what to extract. The model uses its understanding of language and context to locate and return the correct values, even across documents with completely different layouts.

This approach supports two primary modes. In batch extraction (single-pass), all fields are extracted in one LLM call per document — the model receives the complete text along with all field definitions and returns a JSON object with every value at once. This is faster and more cost-effective for straightforward fields. In per-field extraction, each field gets a dedicated LLM call with a focused prompt, allowing more nuanced instructions and higher accuracy for complex or ambiguous fields. DocumentIQ supports both modes and lets users choose per job based on their accuracy and cost requirements.

The key advantages over template OCR are adaptability and zero-config setup. A template system requires someone to manually map regions for every document layout variant — a new vendor invoice format means a new template. LLM extraction handles layout variation inherently because it operates on text meaning, not pixel positions. Combined with few-shot examples from document annotations and a hierarchical prompt system, LLM extraction can be progressively refined without any code changes or model retraining.