I’ve seen claims that Latenode’s AI Copilot Workflow Generation can take a description like “build a workflow that retrieves information from our CRM and summarizes customer interactions” and spit out a working automation. That sounds incredible, but also sounds like the kind of thing that works great in the demo and falls apart on real data.
I’m skeptical, but curious. The pitch is that you describe what you want, the AI understands your intent, and generates a workflow you can use immediately. No coding, no infrastructure knowledge required.
The reason I’m interested is that RAG specifically has a lot of moving parts. You need retrieval logic, data source integration, prompt engineering for synthesis, error handling. If an AI can assemble all of that from a plain description, that changes everything for teams without engineering resources.
But I’m wondering: does the generated workflow actually work on the first try, or is it more like a starting point that needs significant tweaking? And when you have choices to make (like which retrieval model to use when you have 400+ available), does the AI make reasonable defaults, or do you end up making those choices yourself anyway?
Has anyone actually used this feature to generate a RAG workflow and had it work reasonably well, or am I the only skeptic here?
It’s not marketing. I’m being honest: I was skeptical too until I tried it.
The AI Copilot doesn’t generate perfect workflows. It generates working starting points. You describe a RAG workflow, and it creates the basic flow: retrieval component, LLM component, wiring between them. Then you test it, tweak the prompt or data sources, and iterate.
Obviously this works better if you’re detailed in your description. “Retrieve customer support docs and answer questions” gives you something usable. “Retrieve customer support docs from Slack, cross-reference with our knowledge base, and answer questions while tracking which docs you used” gives you something closer to what you actually want, though still with tweaks needed.
For RAG specifically, the Copilot is great because it spares you from building the retrieval-to-generation flow yourself. You’re not starting from a blank canvas. You have a working skeleton in minutes instead of hours. Then the customization is usually just connecting your real data sources and testing.
Does it pick the right retrieval model from 400+ options? No. You make that choice based on your use case. But it gives you a working system immediately so you can experiment.
I used it to generate a basic RAG workflow that pulls from our internal docs and answers questions. The output was about 70% of what I needed. The retrieval logic was there, the LLM integration was there, but I needed to connect it to our actual data sources and adjust the prompt.
The significant part: I had a working system to test against in 10 minutes. Normally that would take me a few hours to architect. The tweaking and tuning still took time, but I wasn’t starting from zero.
It’s not magic, but it does compress the “setup infrastructure” phase into something fast. Whether that’s worth it depends on how comfortable you are building workflows manually.
The AI Copilot for RAG workflows works best when you’re clear about your inputs and outputs. If you say “retrieve documents and summarize findings,” it understands that pattern. The generated workflow handles the basic retrieval and summarization flow. Integration specifics—what your data source actually looks like, how to format the prompts—those need manual adjustment.
I’d call it a 70/30 split. 70% of the workflow is generated correctly. 30% requires tuning based on your specific data.
The AI Copilot generates architecturally sound workflows. It understands the RAG pattern—retrieval followed by generation with context injection. What it doesn’t do is optimize for your specific data or choose between models intelligently. It makes functional defaults and lets humans handle the optimization layer.
It’s a solid starting point, not a finished product. Saves you architecture time but you still need to configure data sources and test. Maybe 60-70% useful as-is.