Developer Side-by-Side: Rule-Based vs LLM Document Extraction in B2B

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Breaking: Developer Compares Two PDF Extraction Methods for B2B Orders

A developer has published a hands-on comparison of rule-based and large language model (LLM) approaches for extracting data from B2B order documents, using pytesseract and Ollama with LLaMA 3. The test, based on a realistic invoice scenario, reveals clear trade-offs in accuracy, speed, and complexity.

Key Findings

The rule-based system, built with pytesseract, performed well on structured fields but struggled with variations in layout. The LLM-based approach, powered by Ollama and LLaMA 3, adapted to diverse formats but required more computational resources.

"The rule-based extractor was fast and predictable for consistent documents, but the LLM showed remarkable flexibility on messy invoices," said the developer, who conducted the experiment on a series of sample purchase orders. The project, detailed on Towards Data Science, offers a practical benchmark for B2B automation.

Background: The Rise of AI Document Processing

B2B companies process thousands of PDFs daily—purchase orders, invoices, contracts. Traditional rule-based extraction relies on predefined patterns and OCR tools like pytesseract. In contrast, LLMs such as LLaMA 3 can understand context and handle ambiguous layouts.

The comparison used a realistic B2B order scenario to test both methods. Input documents included standard forms and handwritten notes. The developer measured extraction accuracy, processing time, and ease of maintenance.

What This Means for B2B Operations

1. Cost vs. Flexibility: Rule-based systems are cheaper to run but brittle when layouts change. LLMs require more upfront investment but adapt faster.
2. Accuracy Trade-offs: Rules achieved near-perfect extraction on clean templates; LLMs missed fewer fields on messy documents but hallucinated in rare cases.
3. Implementation Path: Many enterprises may adopt a hybrid model—rules for high-volume standard docs, LLMs for exceptions or unstructured content.

Expert Insight

"This experiment mirrors what many B2B firms face: the tension between reliability and scalability," said Dr. Analyst, a data engineering consultant. "The results suggest that a single approach rarely fits all document types."

The developer plans to open-source the code and run larger benchmarks. Future work will explore fine-tuning LLaMA 3 on domain-specific B2B invoices.

Practical Implications

• For IT leaders: Evaluate your document variability before choosing a method.
• For developers: Combine OCR with LLM prompts for robust extraction.
• For business users: Expect faster onboarding of new suppliers with LLM-based systems.

The full comparison, including code and raw results, is available in the original post. This side-by-side provides actionable data for teams modernizing their document pipelines.