I’ve been curious about this for a while now. Everyone talks about describing what you want in plain English and getting a ready-to-run RAG workflow back, but I’ve never actually seen it happen in real time.
So I tried it. I wrote out: “I need a workflow that takes customer support tickets, retrieves relevant docs from our knowledge base, and generates a response using the best available model.”
Then I hit generate. And honestly? It actually built something functional. Not perfect, but functional. It created nodes for document retrieval, connected them to an AI model for synthesis, and structured the whole thing so data flows properly.
What surprised me most is that it didn’t just wire things together randomly. It seemed to understand that retrieval needs to happen before generation, that you need error handling between steps, and that the output needs to be formatted a certain way.
The docs mention that the AI Copilot uses the platform’s own AI logic to interpret descriptions and generate workflows. But what I’m really wondering is: how much of this is actually intelligent pattern matching versus just applying common RAG architecture templates? And when the generated workflow doesn’t do exactly what you need, how do you tweak it without basically rebuilding it from scratch?
The Copilot isn’t just templating. It actually understands the intent behind what you describe and builds the right node sequence for it. When you say “retrieve docs and generate responses”, it knows to add validation between steps, handle edge cases, and connect the right model types.
Here’s the thing though - you can absolutely tweak generated workflows. One of the big wins with Latenode is that you can drop into the visual builder and modify any part. Change a model, add error handling, swap data sources. It’s all drag and drop, so you’re not fighting with code.
The real power is that the generated workflow gives you a working baseline. You’re not starting blank. Most teams I’ve seen take the generated workflow, run it once to see how it behaves, then customize it for their specific knowledge sources and model preferences.
I had the same question when I first used this. The Copilot does actually understand context, not just applying templates. But here’s what I learned - it works best when you’re specific about what you’re doing. “Create a RAG workflow” gets you something basic. “Create a RAG workflow that retrieves from Slack archives and responds with product docs” gets you something much closer to what you actually need.
After it generates the workflow, you’ll probably want to test it with real data. That’s where you discover what needs adjusting. Maybe the retrieved documents aren’t relevant enough, or maybe you want to try a different model for generation. The good part is making those changes is visual and straightforward.
The generated workflows are genuinely useful as starting points, but they’re not magic. What the Copilot actually does is map your description to a logical flow - identifying what needs retrieval, what needs generation, and where validation should happen. It uses standard RAG patterns that actually work in practice. The quality of what you get back depends on how clearly you describe what you need. I’d say about 70% of what it generates is immediately usable, and the rest needs refinement. The visual builder makes that refinement relatively painless compared to writing workflow code manually.
The Copilot leverages the platform’s knowledge of common RAG implementations, so it patterns-matches your description against proven workflows. When you describe retrieval and generation requirements, it constructs the appropriate node sequence with sensible defaults. The generated output reflects actual RAG architecture principles - retrieval before synthesis, data flow considerations, error boundaries. You can then iterate through the visual builder without touching code, which fundamentally changes the friction of building these systems compared to traditional development approaches.
Copilot maps your description to standard RAG flows. Not magic, but genuinely useful. About 70% usable as-is, rest needs tweaks. The visual builder makes editing easy compared to doing it manually.