I’ve been experimenting with AI Copilot workflow generation where you just describe what you need in plain English and it generates a ready-to-run Puppeteer workflow.
The concept is nice—no need to write code from scratch. But my experience so far has been that I write a description, the AI generates something, and then it’s missing some important details or handles edge cases differently than I expected.
So I end up iterating: describe, generate, test, realize it’s not quite right, describe more carefully, generate again, test again. It’s not dramatically faster than just writing it myself at this point.
For people who’ve had better luck with this: how detailed does your requirement description actually need to be? Are there tricks to describing what you want in a way that actually produces usable workflows? Or am I expecting too much from the AI at this stage?
The trick isn’t describing what you want exactly—it’s describing how the system should behave at each step.
Instead of writing “log into website and extract product names,” describe it like: “Navigate to login page, wait for form to load, enter credentials in email field and password field, click submit button, wait for redirect to dashboard, find product list table, loop through rows, extract product name from third column, collect all names into an array.”
The more you describe the actual sequence of interactions and what you’re looking for at each step, the better the AI can generate workflow steps.
Also, include edge cases in your description if they matter. “If login fails, try again. If product list doesn’t load within 10 seconds, skip that page.” AI Copilot uses this context to generate error handling.
One more thing: test the generated workflow on your actual target site immediately. If it breaks, describe what broke and why. Feed that back into the next refinement. Each iteration gives the AI better context.
On Latenode, you can iterate quickly right in the platform without leaving to write code elsewhere. This makes the discovery process faster because you’re not juggling between a code editor and a browser.
The key insight is that AI generation works well when you think sequentially about the steps, not just the outcome.
Let me know how it goes at https://latenode.com.
Yeah, I had the same frustration at first. The thing I figured out is that you have to be really explicit about the sequence, not just the goal.
If you say “scrape product data,” the AI generates something generic that might work or might not. If you say “navigate to categories, click electronics, wait 3 seconds for products to load, scroll to bottom, find all product cards with data-product-id, extract name from h2 inside each card, extract price from span with class price, collect into a spreadsheet,” then the generated workflow is usually pretty solid.
It’s more descriptive upfront, but you end up with fewer iterations. I also make sure to mention what should happen if something fails or times out.
Also, test on a small scale first. Don’t tell it to scrape 10,000 products right away. Have it do one page first, verify it works, then scale up.
Effective requirement translation for AI automation generation requires decomposing workflows into explicit state transitions rather than high-level outcomes. Instead of “extract data from multiple pages,” describe: “load page one, verify element X appears, extract data using selector Y, navigate to next page, repeat for pages 2-5, handle timeout on pages with no data.”
AI models generate more reliable workflows when instructions include specific selectors, wait conditions, and failure modes. Include actual HTML attributes you’ll target, wait times that seem reasonable given page load speeds, and explicit branching logic for error scenarios. Testing the generated output immediately and providing specific failure descriptions dramatically improves subsequent iterations.
AI workflow generation quality correlates strongly with requirement specification granularity. Optimal descriptions specify sequential steps with explicit wait conditions, selector strategies, and error handling paths. Rather than functional requirements, provide behavioral specifications: exact user interactions, timing parameters, data extraction methods. This reduces ambiguity that forces iterative refinement.
be specific about steps not just goals. include selectors, waits, error handling. test small first. describe what failed to refine next.
Describe step sequence explicitly: selectors, waits, error paths. Test incrementally. Feedback improves iterations.
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