Structured Data Extraction
The process of transforming unstructured document content into organized, machine-readable data with defined fields and formats.
Structured data extraction goes beyond information extraction to produce outputs that conform to a specific schema — a JSON object, a database table, a spreadsheet row — that downstream systems can process without additional parsing. For a contract, structured extraction produces a consistent schema: {"party_a": "...", "party_b": "...", "effective_date": "...", "term_years": "...", "payment_amount": "...", "governing_law": "..."}.
The challenge in structured extraction is handling the enormous variation in how the same information is expressed across documents. "This Agreement shall commence on..." and "The term begins on..." and "Effective as of..." all express the same concept in different ways. Modern LLM-based extraction handles this variation far better than traditional rule-based or template-matching approaches, enabling structured extraction across heterogeneous document sets without requiring document-type-specific templates.
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Retrieval-Augmented Generation (RAG)
An AI architecture that combines information retrieval with text generation to produce answers grounded in source documents.
Vector Embedding
A numerical representation of text as a high-dimensional vector, enabling semantic similarity comparisons between passages.
BM25
A probabilistic keyword-ranking algorithm that scores documents by term frequency and inverse document frequency.
Chunking
The process of splitting large documents into smaller, overlapping segments optimized for retrieval and embedding.
Hallucination
When an AI model generates plausible-sounding but factually incorrect or fabricated information.
Large Language Model (LLM)
A neural network trained on massive text corpora that can understand and generate human language.
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