How I Stopped Fighting Hallucinations in LLM Data Extraction

Short summary

Author reduced LLM hallucinations in invoice extraction from 40% to near-zero by pairing Pydantic schema validation with immediate retry-on-error feedback loops, replacing pure prompt engineering. Trade-offs: 5-second latency per invoice and higher API costs; single schema fails for heterogeneous document types.

• •Pure prompt engineering yields only 60–70% accuracy; hallucinations include wrong field names, invented line items, format inconsistencies
• •Production solution: strict Pydantic schema + OpenAI JSON mode + immediate validation + retry with error context
• •Cost and latency scales with retries; requires per-document-type schemas for diverse inputs

Generated with AI, which can make mistakes.