I Built the Same B2B Document Extractor Twice: Rules vs. LLM

Editor's take

A developer built two systems for extracting data from B2B documents: one using traditional rule-based methods with pytesseract, and another leveraging an LLM, LLaMA 3 via Ollama. This practical comparison highlights the trade-offs between rigid, predictable extraction and the more flexible, emergent capabilities of large language models in handling unstructured data.

The significance lies in the ongoing debate about LLM adoption for enterprise tasks. While rule-based systems offer precision for well-defined formats like invoices or purchase orders, LLMs promise adaptability to variations and complex interpretations. This experiment directly addresses the real-world applicability and potential cost-efficiency of LLMs against established OCR and pattern matching techniques, impacting businesses that rely on automated document processing.

Future developments to monitor include the scalability and robustness of the LLM approach across a wider range of document types and quality. Specifically, observing how LLaMA 3's performance holds up with degraded scanned documents or highly idiosyncratic B2B formats will be crucial. A significant shift in view would occur if the LLM consistently outperformed the rule-based system in terms of accuracy and reduced manual intervention across diverse, real-world enterprise datasets.