I’ve been trying to wrap my head around how this actually works in practice. The idea of describing what I want in plain English and having it generate a RAG pipeline sounds amazing, but I’m skeptical about what gets left out in translation.
Like, when I say “fetch customer policies and generate responses,” does the Copilot understand that I need to set up the retrieval part separately from the generation part? Or does it just wire things together and hope for the best?
I’m curious whether anyone here has actually tested this with a real workflow. Does it handle the nuances of RAG—like choosing which model retrieves versus which one generates—or does it just create shells that need heavy customization?
What’s been your experience with how complete the generated workflows actually are?
The AI Copilot is actually pretty solid at understanding RAG structure from plain language. When you describe retrieving policies and generating responses, it maps that to actual workflow nodes—retrieval node, generation node, data connections.
What makes this work in Latenode is that you’re not managing vector databases yourself. The platform handles that layer. So when you describe the logic, the Copilot can focus on wiring retrieval and generation together without getting tangled in infrastructure.
I’ve seen it generate workflows that handle multi-step RAG—pulling from multiple sources, ranking results, then feeding into generation. Not perfect every time, but usable as a starting point.
The real advantage is that you can iterate fast. Generated something that doesn’t quite work? Adjust the description and regenerate. Or jump into the visual builder and tweak nodes directly.
Try it yourself on https://latenode.com
I tested this recently with a support ticket workflow. Described something like “pull from our knowledge base and reply to customers in our brand voice.” The Copilot generated about 70% of what I needed.
It got the basic flow right—retrieval, then generation. But the prompt engineering part? That needed tweaking. The generated prompt wasn’t quite capturing our voice.
What surprised me was how much it understood about data flow. It connected the right outputs to inputs without me specifying exact field names. That’s the part that usually kills manual workflow building.
The gaps weren’t in logic. They were in tuning. Which, honestly, you’d have to do anyway even if you built it from scratch.
The Copilot handles the structural part well. It understands that RAG has a retrieval phase and a generation phase, and it maps those to actual nodes. Where it gets fuzzy is with edge cases—what happens if retrieval returns nothing? How should the generator handle low-confidence matches?
Those aren’t failures though. They’re just the parts where you need to think about your specific use case. The Copilot gives you a foundation that works, then you add guardrails based on what matters for your data.
I’d say expect 60-80% functional right out of generation, depending on how specific your description is. Better descriptions lead to better workflows.
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