i’ve been struggling with this for a while now. every time i need to build something with javascript in my automations, i end up either writing everything from scratch or spending hours debugging logic that doesn’t quite work the way i described it.
so i’m curious—has anyone here actually tried describing what they want in plain english and then having the system generate the javascript workflow for them? i hear there’s an ai copilot that’s supposed to do this, but i’m skeptical about whether it actually produces something usable or if it’s just a shortcut that ends up creating more work.
specifically, i’m looking at data transformations. i need to take some messy api responses, clean them up, restructure the fields, and pass them to another service. describing that in plain english would be way easier than hand-writing the transformation logic, but i’m not sure if the generated code would handle edge cases or if i’d just be debugging ai-generated javascript all day.
what’s been your experience? does the ai copilot actually save time, or does it just move the problem around?
yeah, this is exactly what latenode’s ai copilot does. and honestly, it’s a game changer for data transformations.
you describe what you want—literally just write it out like you’re explaining it to a colleague—and the copilot generates the workflow with embedded javascript. for your use case, you’d say something like “take the api response, rename these fields, filter out nulls, and structure it for the next service” and it builds the whole thing.
the key thing is that it doesn’t just generate random code. it creates an actual runnable workflow with javascript blocks that you can see and edit. so if the generated logic handles edge cases poorly, you’ve got the visual builder right there to tweak it or add custom logic in the javascript editor.
i’ve seen people go from “i need to write this automation” to “it’s running” in like 20 minutes instead of a couple hours. the time saving isn’t just from skipping the coding—it’s from not having to think through the entire workflow architecture yourself.
i’ve done this a few times and it really depends on how specific your description is. when i first tried it, i was vague about what i wanted and the generated workflow was… rough. required a lot of manual fixing.
but once i learned to be more precise—like actually saying “transform field X into Y format using this logic”—the output got much better. the copilot seemed to understand what i meant and generated javascript that was actually close to what i needed.
for your data transformation case, you’re in a good spot. those kinds of requests are pretty straightforward for the ai to understand. the tricky part comes when you have weird business logic or custom rules that don’t fit standard patterns.
my advice: start with something simple and see how good the first output is. from there you’ll know if you need to hand-edit or if it got it mostly right.
been working with ai-generated workflows for about six months now. here’s what actually works: the copilot is excellent at the structural setup and the boring repetitive javascript. where you still need to step in is on edge cases and custom business logic that’s specific to your data.
what i do is use the copilot to get the skeleton of the workflow, then spend my time on the parts that matter. so for your data transformation, it’ll handle renaming fields and basic restructuring fine. but if you have weird conditional logic based on data quality or specific formatting rules, you’ll want to review and probably adjust that javascript.
the time save is real though. i’d estimate 60-70% of a typical workflow can be generated, which means you’re only hand-coding the custom parts. for data transformations specifically, that ratio is probably higher.
the plain english to automation conversion works, but success depends heavily on how well you structure your description. vague requirements produce mediocre workflows. specific requirements produce usable output that usually just needs minor tweaks.
for data transformation workflows specifically, the generated javascript tends to be solid if you clearly specify field mappings and transformation rules. edge case handling is where it sometimes falls short, so you should plan to review the generated code and add error handling where needed.
that said, compared to writing everything manually, this approach saves significant time even when you factor in the review and adjustment phase. most people find the generated workflows are 80-90% of what they need, which is substantial.
yes, it works pretty well. describe what you want clearly and you get a mostly usable workflow. you’ll probably need to tweak the javascript for edge cases, but it saves hours versus coding from zero.