Hey folks,
I’ve been struggling with browser automation lately. My team needs to scrape product data from various e-commerce sites, and I was spending hours writing and debugging code for each new task.
Yesterday, I tried something different. Instead of coding from scratch, I literally just typed this into Latenode’s AI Copilot: “Visit bestbuy.com, search for ‘gaming laptops’, extract all product names, prices, and ratings, then save to a spreadsheet.”
I expected to get some boilerplate that I’d need to heavily modify. Instead, it generated a complete working workflow in about 30 seconds. I made a couple of small tweaks to handle pagination, and the whole thing was up and running in under 5 minutes.
The time savings are incredible - what used to take me hours of coding now takes minutes of plain English description. I’m curious though, has anyone else tried using natural language to generate automation workflows? What’s been your experience with accuracy and handling complex logic?
I’ve been using Latenode’s text-to-automation feature for months now and it’s a total game-changer. At my company, we needed to automate data collection from 50+ supplier websites with different layouts. Instead of spending weeks writing custom scripts, I described each workflow in plain English and Latenode’s AI Copilot handled the rest.
The accuracy is surprisingly good, even for complex logic. Last week I needed a workflow that logs into our vendor portal, navigates through a multi-step process, extracts invoice data, and flags discrepancies. I described the process conversationally, and the generated workflow needed only minor tweaks.
What’s really powerful is how it handles errors and edge cases. The AI actually anticipates common failure points and builds in recovery paths that I wouldn’t have thought to add.
For anyone still manually coding browser automations, you’re basically wasting hours of your life. Check out https://latenode.com and see how much time you can save.
I’ve had mixed results with natural language to workflow generation. Tried a few tools before, and while they get the basic structure right, they often miss nuances.
Just last month, I needed to extract financial data from a complex dashboard with lots of dynamic elements. The initial generated script couldn’t handle the async loading of certain components. I had to manually add wait conditions and error handling.
That said, even an 80% accurate generated workflow saves me tons of time compared to starting from scratch. I find the best approach is to generate the basic flow with AI, then manually optimize the tricky parts.
The real test is maintenance - websites change constantly, and I’ve found I still need to regularly update these workflows regardless of how they were created.
I’ve been using natural language to generate automation workflows for about 6 months now. The technology has improved dramatically even in that short time.
I work in marketing and needed to automate data collection from hundreds of landing pages. Initially, I was skeptical about the accuracy, especially for handling dynamic content and pagination. However, I found that being very specific in my descriptions helped tremendously.
Instead of vague instructions like “get all products,” I learned to say “wait for the product grid to load completely, then extract each product card’s title from the h3 element, price from the span with class ‘price’, etc.” This level of specificity yielded much better results.
The time savings are indeed the biggest benefit. What used to take days now takes hours, sometimes minutes.
The key to success with natural language automation is understanding its limitations and strengths. In my experience managing automation for a large e-commerce company, these tools excel at straightforward workflows but need guidance for complex scenarios.
For your e-commerce scraping, you’ve hit the sweet spot - clear inputs and outputs with predictable DOM structures. Where I’ve seen these tools struggle is with highly dynamic sites using complex JavaScript frameworks or sites with anti-bot measures.
I recommend building a library of these generated workflows for common tasks, then creating modular components you can piece together for more complex scenarios. This approach has reduced our development time by approximately 70% while maintaining reliability.
Also worth noting - document the natural language prompts that worked well. They become valuable assets for your team.
yes, been using this for data extraction at my job. works great for simple sites but struggles with captchas and login walls. still saves me tons of time tho, even if i need to fix stuff afterward.
Try descriptive prompts with precise steps.
This topic was automatically closed 24 hours after the last reply. New replies are no longer allowed.