I’ve been curious about this for a while. We have tons of documentation scattered across different sources, and I’ve heard RAG is supposed to help answer questions by pulling from those docs. But the idea of building a whole retrieval-augmented generation system sounds complex.
Then I read about Latenode’s AI Copilot Workflow Generation, where you apparently just describe what you want in plain text and it generates a ready-to-run workflow. So I’m wondering—does it actually create something functional that can fetch data from multiple sources and give cited answers? Or is it more like a starting template that still needs serious work?
Has anyone here actually tested this? What does the generated workflow actually look like when you give it a RAG problem?
I’ve tested this a few times and it’s genuinely impressive. You describe something like “create a workflow that answers questions by pulling from our three knowledge bases and citing sources” and it actually generates the retriever, ranker, and generator blocks all wired up.
The workflow it creates isn’t just scaffolding. It’s functional. You can run it immediately. What surprised me is how it handles the multiple sources—it sets up the right blocks to fetch from different places and then aggregate the results before generating the answer.
You might need to swap in your actual data sources and maybe tweak the model choices, but the hard part—understanding how to structure retrieval, ranking, and generation—that’s all handled. Saves a ton of time compared to building it from scratch.
I’ve used it and the output is surprisingly solid. The AI Copilot understands RAG concepts well enough to wire everything together logically. It figures out where the retriever block needs to sit, adds a ranker if it makes sense, and puts the generator at the end.
The thing is, it doesn’t just drop a template on you. It actually reads your description and tailors the block configuration. If you mention you need citations, it sets that up. If you say multiple sources, it creates the fetch logic accordingly.
That said, you’ll still need to configure your actual data connectors and maybe adjust some parameters. But the architectural thinking—that heavy lifting—it really does handle.
Real question for you: have you considered starting with one of the marketplace templates instead of describing it to the Copilot? I found that most RAG implementations follow pretty similar patterns, and having a working example to modify might actually be faster than generating from scratch, even with the Copilot.
That said, what the Copilot generates is definitely functional. It’s not just a skeleton. It understands block dependencies and creates something that actually runs. People tend to underestimate how much architectural work goes into a RAG system, and the Copilot handles that thinking for you.
Yes, it generates working RAG pipelines. The Copilot creates functional retriever, ranker, and generator blocks. You’ll adjust data sources and maybe tweak models, but the architecture is solid from the start.