B2B Document Extraction Showdown: Rule-Based vs LLM – New Analysis Highlights Trade-offs
A head-to-head comparison of two approaches to B2B document extraction has revealed critical differences in accuracy, speed, and adaptability. The analysis, published on Towards Data Science, compares a rule-based system using pytesseract with an LLM-based system using Ollama and LLaMA 3.
“The results show that while both methods can extract structured data from PDF orders, they excel in very different scenarios,” stated the anonymous developer behind the study. “The rule-based approach is faster and more predictable, but the LLM handles unexpected formats much better.”
Background
B2B document extraction is a common pain point for companies that process large volumes of PDF orders. Traditional rule-based methods rely on predefined patterns, such as regular expressions and positional coordinates, to extract fields like order numbers, line items, and totals.
The LLM-based alternative uses a large language model fine-tuned for document understanding. In this test, the developer ran LLaMA 3 locally via Ollama, feeding it raw PDF text extracted by pytesseract. The LLM was prompted to identify and structure the required fields without explicit rules.
“The test document was a realistic B2B purchase order with multiple line items, headers, and a footer – exactly the kind of messy input that breaks simple parsers,” explained the source. “I wanted to see which method could handle the chaos better.”
What This Means
For businesses, the choice between rule-based and LLM extraction now has clearer implications. Rule-based systems offer deterministic output and lower latency, ideal for high-volume, standardized documents. However, they fail when document layouts vary.
LLM-based systems, while slower and more resource-intensive, adapt to novel structures without reprogramming. “This trade-off means companies with stable document formats should stick to rules,” the developer noted. “But if you get 20 different suppliers each with their own template, LLMs will save months of maintenance.”
The analysis also highlighted that LLMs can misinterpret ambiguous fields, requiring post-processing validation. In the test, the rule-based extractor achieved 100% accuracy on conforming documents, while the LLM made two errors out of ten line items – but also correctly parsed a non-standard field the rules missed entirely.
“No single approach is perfect,” the source concluded. “The winning strategy likely involves a hybrid: use rules for the 80% of documents that are standard, and fall back to an LLM for the outliers.”
As B2B digitization accelerates, this comparison offers a practical roadmap for teams evaluating their extraction stack. The full breakdown is available on Towards Data Science, with code and test data included for replication.