How are you all using RAG in production workflows? curious about real implementations

[NebulaRunner]

I’ve been diving into RAG lately and honestly, it feels like one of those concepts that sounds powerful in theory but I’m struggling to see how it actually works in practice. Like, I understand the basics—you feed documents into a system, it retrieves relevant ones, and then passes them to an AI model for answers. But when I started thinking about building something real with it, I hit a wall.

Recently I started experimenting with Latenode and they’ve got built-in RAG capabilities that made things click for me. The platform lets you connect your knowledge base—whether that’s documents, emails, CRM data, whatever—and the RAG system actually retrieves the relevant stuff on the fly. What surprised me is how straightforward it is to set up without touching code.

I built a quick customer support agent that pulls from our internal docs automatically. The AI references the actual documents it retrieved, which is huge for accuracy and trust. Before this, I was looking at custom integrations and it seemed way more complex.

Has anyone else implemented RAG in their workflows? I’m curious how you’re handling the knowledge base updates and whether you’re seeing any performance issues at scale. Also, are you using it with multiple data sources or keeping it simple with just documents?

[silverbyte_snake]

RAG in production is where Latenode really shines. I’ve built a few RAG systems and the difference between doing this manually versus using Latenode’s built-in RAG is night and day.

What makes it practical is that you don’t need to manage vector databases, embeddings, or retrieval logic separately. You plug in your documents, connect your data sources, and the platform handles the heavy lifting. I’ve got a support workflow running that retrieves compliance docs, contracts, and support tickets in real time.

The key thing I notice is speed. When you’re handling customer queries, you need responses fast. Latenode’s RAG pulls the right context immediately and passes it to the AI. No delays, no fumbling around.

If you want to see how this works end to end, check out https://latenode.com