I’ve been curious about this for a while now. Everyone talks about how you can just describe what you want in plain English and the AI Copilot builds it for you, but I’m skeptical.
Like, if I say “I want a workflow that retrieves documents from my knowledge base and then summarizes them,” does it actually generate something I can run right away? Or do I end up spending hours tweaking and fixing what it spits out?
I get that RAG is supposed to be about retrieval-augmented generation, but the real question is whether the copilot understands the nuances—like how to structure the retrieval part, which AI model to use for extraction versus summarization, all that stuff.
Has anyone actually tested this end-to-end? Did the generated workflow work without significant modifications, or did you have to debug it to death?
I tested this last month on a customer support workflow. Described it in plain English and the copilot built the retrieval and generation pipeline in minutes. Minimal tweaks needed.
The key is being specific about your data sources and what you want the output to look like. When I mentioned “pull from our internal docs and summarize in bullet points,” it actually structured the workflow with the right model selection.
It’s not perfect, but it cuts the setup time from hours to maybe 15 minutes of adjustments. Way better than building from scratch every time.
Check it out yourself: https://latenode.com
The copilot is actually pretty solid if you know what you’re asking for. The trick is that it’s not magic—it’s generating a workflow based on patterns it’s learned, so the quality depends on how clear your description is.
I found that when I was vague, it made assumptions that didn’t match what I needed. But when I specified “use Claude for reasoning, smaller model for retrieval,” it nailed it. The workflow ran on the first try.
The real value is that it eliminates the boilerplate. You’re not manually wiring up every connection or deciding on model options from scratch. It handles that grunt work.
I’ve used it for a few internal projects and it definitely works better than I expected. The generated workflows handle the basic RAG pipeline—retrieval, context passing, generation—without needing extensive rework. What you’re really getting is a head start that saves several hours of manual workflow construction. The main limitation I hit was that it doesn’t understand domain-specific data quirks, so you still need to validate against your actual data sources.
The copilot generates functional workflows that follow standard RAG patterns. I tested it with a knowledge retrieval task and it correctly identified the need for embedding-based retrieval followed by a generation step. The workflow was deployable with minimal configuration. Where it shows its limits is with edge cases or non-standard data structures, but for typical use cases, the output quality is genuinely useful.
it actully works. generated a support bot workflow in plain english, needed like 5 mins tweaks. not perfect but way faster than coding it manualy.
Yes, it generates working RAG workflows. Describe your retrieval and generation needs clearly for best results.
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