When Regex Fails: LLMs for Messy HTML Data

Last month I inherited a project that needed to extract product information from a legacy e‑commerce...

venerdì 12 giugno 2026 New tab

998 words~5 min read

Last month I inherited a project that needed to extract product information from a legacy e‑commerce site. The HTML was a nightmare—no semantic classes, inconsistent attribute names, and the occasional blob of inline JavaScript. I thought I could just write a few regular expressions and be done in an hour. Six hours later I was staring at a wall of conditional logic that broke every time the page changed.

I needed a better way, and I ended up using a large language model (LLM) to handle the fuzzy extraction. Here’s what I learned—dead ends included—and a working approach you can copy‑paste today.

The Problem

The site had product cards like this:

When Regex Fails: LLMs for Messy HTML Data

When Regex Fails: LLMs for Messy HTML Data

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