The article addresses a common question: should you swap a regex-based parser for an ML model when extracting fields from PDFs? The author argues that rule-based approaches remain a strong default for many real-world cases, especially when the documents are well-structured and the extraction logic is stable. Regex is transparent, easy to debug, and requires no training data or model maintenance.
However, ML becomes attractive when the input variability is too high for handcrafted rules — for example, when layouts shift across vendors or fields appear in unpredictable orders. The trade-off is that ML models need labeled examples, careful evaluation, and ongoing retraining as data evolves. The article suggests that teams should weigh the complexity of the rules they are maintaining against the cost of building and operating a model.
In short, the author's advice is to start simple with regex and only move to ML when there is concrete evidence that rules are failing or becoming unmanageable. The decision should be driven by error rates, business impact, and the availability of quality training data, not by the novelty of the technology.