I’ve been trying to wrap my head around RAG for a while now, and I finally decided to stop reading blog posts and just build something. I started playing around with Latenode’s visual builder, and honestly, it’s way less intimidating than I thought it’d be.
The thing that clicked for me was realizing RAG isn’t some magical black box—it’s just retrieval plus generation wired together. In Latenode, I could see the whole pipeline: you describe what you want, the AI Copilot spins up a workflow graph, and then you’re just connecting blocks. One block fetches your knowledge base, another block ranks or filters results, and then you wire it into a generation step.
But here’s where I got stuck: how much of the workflow should I let the copilot generate versus building it myself? I tried the AI Copilot first with a plain English description of “build a Q&A bot that searches our company docs and answers questions.” It actually generated something usable, though I had to tweak the vector store connection and swap out a couple of models.
What I’m curious about is whether people are finding that the auto-generated workflows from plain text descriptions are actually production-ready, or if everyone ends up heavily customizing them? And when you do customize, how much are you actually changing—just the models, or are you rebuilding the retrieval logic too?
The AI Copilot generates a solid foundation, and that’s the win. You get the structure right without overthinking it.
What most people miss is that Latenode lets you test different models for retrieval and generation without API key hell. Swap a model, run it, see if it performs better. You can’t do that easily anywhere else because you’d need separate accounts and billing for each model provider.
I typically use the generated workflow and then iterate on model selection. The retriever needs speed, the generator needs quality. With access to 400+ models in one place, you can prototype fast.
The visual builder makes it obvious when retrieval is weak versus when generation is poor. That feedback loop alone is worth it.
Check out https://latenode.com for more on how to approach this.
I found that the copilot-generated workflows handle the boring wiring for you, which saves time. But the real customization happens around how you handle your actual data.
When I built a document QA system, the template got the retrieval and generation blocks right, but I had to adapt it to how our knowledge base was actually structured. Some of our docs had metadata that mattered for filtering, and the generic workflow didn’t account for that.
So my approach now is: let the copilot do the heavy lifting for structure, then spend time on the data layer. That’s where your actual business logic lives.
This topic was automatically closed 24 hours after the last reply. New replies are no longer allowed.