I’ve been wrestling with scraping dynamic content from a few sites that use modern rendering engines, and the traditional approach of writing Playwright scripts manually is getting tedious. I stumbled onto using plain text descriptions with an AI Copilot to generate the workflows, and I’m curious if this actually works in practice or if I’m just setting myself up for disappointment.
The appeal is obvious—describe what you want (extract all product prices from this dynamically loaded section) and let the AI generate a working scraper without touching code. But here’s what I’m unsure about: does the copilot actually understand webkit rendering delays, wait states, and the quirks of pages that load content after the initial DOM renders?
I’ve tried a few manual approaches before, and the biggest headache is always waiting for content to actually appear. Selectors change, elements load late, sometimes things load in a different order depending on network conditions. If I’m just describing the task in plain English, can the AI actually account for that complexity, or does it generate something that works once and then breaks?
Has anyone tried using a copilot-style tool to generate webkit scrapers from descriptions? What’s the real success rate before you have to jump in and fix things? And more importantly—when things break because the site updated or rendering changed—how much manual tweaking ends up being required?
I’ve done this exact thing multiple times, and it’s way more reliable than you’d expect. The trick is being specific about what you’re describing. Say something like wait for the .product-list container to appear, then extract prices from elements with data-price attribute instead of just get prices.
The AI Copilot in Latenode learns the rendering behavior from your description. When you mention wait states or dynamic loading, it builds those into the workflow automatically. I had a workflow scraping an e-commerce site that loads results on scroll, and just describing that behavior made it handle the async loading correctly.
The stability question is real though. When sites redesign, you’ll need to update the description or the selectors. But the workflow itself stays mostly intact because it’s based on the logic you described, not brittle CSS selectors alone.
Start with a simple description, test it a few times, then refine based on what breaks. You can adjust the workflow directly too if you need precision.
I’ve found that the success really depends on how you frame the problem. If you’re vague about timing—just saying get this data—then yeah, you’ll get something that works maybe 60% of the time. But when I started specifying exactly what I’m waiting for and in what order, things stabilized significantly.
One thing that helped me: I describe not just what to extract, but what tells me the data is actually ready. Instead of scrape product listings, I say wait until the loading spinner disappears, then scrape product listings from the visible container. That extra context makes a huge difference.
The maintenance side is where it gets tricky. I’ve got workflows running for six months where I only needed to tweak them twice. But that’s because the sites didn’t change their structure much. If a site redesigns its layout, you’re going to spend time updating either way—whether you wrote the code manually or described it to an AI.
The copilot will generate something that technically works, but there’s almost always a gap between what you described and what actually runs smoothly in production. I’ve seen workflows where the AI nailed the logic but missed edge cases—like what happens when content loads partially, or when there’s a network hiccup mid-scrape.
My approach now is to treat the copilot output as a starting point. It saves me from writing boilerplate, which is valuable. But I always run it against the live site a few times before considering it done. The real payoff is not having to write the entire thing from scratch, not getting something that works perfectly first try.