So I had this task: scrape product data from a dynamic e-commerce site that heavily relies on webkit rendering. The pages load content in chunks, and using traditional selectors kept failing because elements weren’t there when my script looked for them.
Instead of writing another brittle automation from scratch, I decided to try describing what I needed in plain language and seeing if the copilot could generate something usable. I basically wrote: “Visit the product listing page, wait for dynamic content to load, extract product name and price from each item, and handle pagination.”
Honestly, I was skeptical. But the generated workflow actually caught the timing issues I usually have to debug manually. It included waits for specific selectors and even handled the pagination logic without me spelling it out step by step.
That said, it wasn’t perfect. The first run had some selector issues because the HTML structure was slightly different than what the copilot inferred. I had to go in and adjust a couple of element identifiers, but that took maybe ten minutes instead of the few hours I’d normally spend building it from scratch.
The real win was that I didn’t have to think through the entire flow myself. The copilot handled the structure, the sequencing, and the timing logic. I just validated and refined it.
Has anyone else tried this approach? I’m curious whether your experience with plain text to automation was smoother or if you hit similar rough spots with the generated selectors.
This is exactly the kind of workflow that makes sense with the AI Copilot. You described your need and got a working starting point instead of a blank canvas.
What you ran into with selector adjustments is normal. The copilot generates solid structure, but every site has its quirks. The good part is you only tweaked it, not rebuilt it.
One thing worth trying next time: if you’re dealing with multiple different e-commerce sites with similar layouts, you could use the same plain text description but swap between different AI models for the generation step. Some models are better at inferring timing logic, others are better at CSS selector patterns. Having access to 400+ models through one subscription means you can experiment without juggling API keys.
Also, if you notice patterns across your fixes (like “this site always nests product info inside a shadow DOM” or “pagination is always a button with class .next-page”), you could refine your prompt or even create a template from your working version. Then reuse it next time.
For details on how to set this up and explore the copilot functionality, check out https://latenode.com
The selector drift issue you mentioned is something I’ve dealt with a lot. I found that instead of relying on the copilot to get selectors right on the first pass, I actually treat it more like a template generator.
What helped me was adding a validation step into the workflow itself. After the copilot generates the initial selectors, I run a quick test on a small subset of pages first. That way I catch selector mismatches early before the full crawl runs and wastes time on bad data.
One other thing: if the site you’re scraping changes its HTML structure frequently, you might want to build in some fallback selectors. The copilot can help with that too—just describe in your prompt that you need multiple ways to find the same element, ranked by preference. That’s added resilience without much extra setup.
This experience highlights a key tension: the copilot saves you the architectural work, but you still need to own the details. I think that’s actually the right tradeoff. Most of the time spent building automation by hand isn’t on the big picture—it’s on getting timing right, handling edge cases, and debugging why selectors don’t match. If the copilot handles that plumbing and you just refine the specifics, you’re already ahead.
What I’d suggest for your next iteration is to document what you changed after generation. If you had to fix three selectors, take note of why they were wrong. Was it because the copilot guessed the class name? Was it because the structure was nested differently? That feedback loop helps you write better plain text descriptions the next time around.
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