I’ve been trying to wrap my head around how RAG actually works in practice, and I think the best way for me to learn is to just see it step by step. I know the theory—you retrieve documents, then generate an answer—but when you’re building it visually in Latenode, what does that actually look like? Like, where do the documents come from? How does the system rank them? And then how does the generator actually use that ranked set to create a response?
I’m specifically curious about how Latenode’s visual builder handles the coordination between retrieval and generation. Do you build it as a linear flow, or can autonomous agents handle different parts simultaneously? And if I’m using multiple AI models from that 400+ pool, how do I decide which one goes where in the pipeline?
Has anyone actually built this end-to-end without touching code and lived to tell about it?
The visual builder makes this way simpler than you’d think. You start with a retrieval node that connects to your knowledge base, then feed those results into a ranking node if you want quality control, and finally connect that to a generation node. The beauty is you can pick different AI models for each step from the 400+ available.
I’ve built several of these and the flow is: trigger → retrieve documents → rank by relevance → generate answer → validate → output. Latenode’s no-code builder handles all the data passing between steps automatically.
If you want autonomous agents coordinating these steps, you can set up multiple agents where one retrieves, one ranks, and one generates. They work in sequence or parallel depending on your setup.
The real advantage is you don’t manage separate API keys or billing for each model. Everything runs through one subscription.
Start here and see how it clicks: https://latenode.com
I built a RAG workflow last quarter and honestly, the linear approach is usually the way to go for most cases. You drag a retrieval block, connect it to a ranking block, then to generation. Each block has configuration options where you pick your AI model.
The tricky part isn’t the visual workflow—it’s deciding which retriever and generator pairing actually works for your data. I spent more time testing different model combinations than I did building the workflow itself.
One thing that helped me was starting with a marketplace template. They come pre-configured with sensible defaults, which meant I could see how all the pieces connected before I started customizing it for my actual knowledge base.
The workflow visualization in Latenode is actually quite straightforward. Each block represents a distinct operation—retrieval, reranking, generation—and you connect them with data flows. When I built my first RAG system, I was surprised how clear the data transformation was at each step. The retrieval block outputs a structured list of documents, the ranking block filters and scores them, and the generation block receives that as context. What makes it powerful is that you can inspect the data flowing between blocks in real-time while testing, which helped me debug issues quickly without needing to understand backend code.
The architecture you’re describing maps cleanly to Latenode’s node-based paradigm. Each component—retriever, reranker, generator—becomes a discrete node in your workflow. The key insight is that Latenode abstracts away the vector database complexity. You connect your knowledge source, specify retrieval parameters (like number of results, relevance threshold), and the system handles embeddings and search internally. For model selection across the 400+ pool, I recommend starting with Claude for generation since it handles context well, and OpenAI’s embeddings for retrieval. This pairing has worked reliably across different knowledge bases I’ve deployed.
Docs flow through retrieval→rank→generate. Pick AI models per step. It’s all visual drag-drop. Test early with real queries.
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