How do you implement LLM-powered data extraction and transformation?
LLM-based data extraction converts unstructured text into structured data with high accuracy on diverse inputs. Use cases: extract structured fields from emails (sender intent, key dates, action items), parse resumes into structured candidate profiles, extract financial data from earnings reports, convert meeting transcripts into action items, and parse legal documents into clause databases. Implementation: define the output schema (JSON structure with field names, types, and descriptions), craft extraction prompts with clear instructions and 2-3 examples, use structured output mode for guaranteed format compliance, and implement validation on extracted data. Accuracy techniques: provide detailed field descriptions with edge case guidance, include negative examples (what NOT to extract), implement multi-pass extraction (extract, then verify/correct), and use consensus across multiple extraction attempts for critical data. Batch processing: for high-volume extraction, implement async processing queues, batch similar documents for consistent extraction, cache extraction templates per document type, and implement incremental processing for updating extractions when documents change. Data transformation: LLMs excel at complex transformations that are hard to express as rules: normalize addresses to standard format, classify free-text descriptions into categories, resolve entity names to canonical forms, translate industry jargon into standard terminology, and merge data from multiple sources with conflict resolution. Quality assurance: implement confidence scoring per extracted field, route low-confidence extractions to human review, track extraction accuracy by field and document type, and maintain a golden dataset for regression testing. Cost optimization: use smaller models for simple extraction tasks (GPT-4o-mini handles structured data extraction well), cache results for repeated document types, and implement extraction templates that reduce prompt size.