I’ve watched some demos of no-code workflow builders, and they usually look clean and simple until you try to build something real. That’s when you realize the abstraction is hiding complexity, or worse, preventing you from doing what you actually need.
With RAG, there are concrete steps: ingest your documents, create embeddings, store them somewhere retrievable, handle user queries, fetch relevant chunks, rank or filter them, pass them to an LLM, and return an answer. Some workflows add extra layers—re-ranking, verification, fallbacks when retrieval fails.
I’m trying to understand what building this looks like in Latenode’s visual builder without touching code. Are you dragging components onto a canvas? Do you wire them together by connecting inputs and outputs? Can you see the actual data flowing through each step, or is it abstracted away?
Most importantly, when you need to customize something—changing how documents are chunked, adjusting retrieval logic, modifying the prompt sent to the LLM—how deep can you go without switching to code mode?
Anyone here actually built a full RAG workflow end-to-end in the visual builder? What’s the reality of that experience?
The visual builder shows you every step. You drag in a document source, wire it to an embedder, connect that to storage, then chain retrieval and generation nodes. Data flows visually between components, so you can see what’s happening at each stage.
Customization doesn’t require code unless you want it. You adjust retrieval depth, prompt templates, and filtering through visual fields. If you need deeper customization, JavaScript support is there, but most RAG workflows don’t need it.
The real power is clarity. You’re not juggling code files and API integrations. You see the pipeline, understand the flow, and modify what needs changing.
I built a knowledge base Q&A system end-to-end in the visual builder, and the experience was straightforward. Document upload connected to a retrieval node, which fed into a generator node, which output the answer. Each node had configurable fields for things like “max documents to retrieve” and “temperature for response.”
When I wanted to adjust how documents are ranked before generation, I didn’t need to code. The retrieval node had built-in ranking options. For truly custom logic, JavaScript was available, but I didn’t need it.
The biggest difference from traditional development is that everything is visible and connected visually. You’re not debugging API calls or wondering why a component failed silently.
The visual builder abstracts the infrastructure but doesn’t hide the workflow logic. I connected four main components: document storage, retrieval logic, a re-ranker, and generation. Each component was configurable through the UI. The builder prevented common mistakes—you can’t wire incompatible components together. When data formats don’t match, it tells you immediately. This made debugging faster than traditional APIs where you discover mismatches at runtime.
The visual builder in Latenode uses a node-and-edge paradigm. Each RAG component becomes a node with configurable ports. Document ingestion, embedding, retrieval, and generation are distinct nodes you wire together. Customization happens through node configuration UI and optional JavaScript for advanced cases. The visual representation also serves as documentation—future team members can understand the workflow by looking at it rather than reading code.
Drag nodes for retrieval, generation, storage. Wire them together. Configure each through UI. JavaScript available for advanced logic, but usually not needed.