I keep hearing that you can build RAG workflows visually without touching code, but I’m skeptical about what ‘visually’ actually means. Does it mean dragging predefined blocks and that’s it? Or are you still writing prompts and configuring AI models behind the scenes?
I’m timing myself. I want to know: how long before I have something functional? Not polished, not production-ready, but something I can point at and say ‘this retrieves docs and generates answers.’
The constraint I’m working with is time. I have maybe a few hours to prove this concept works before I go deeper. If the visual builder actually cuts setup time down from weeks to hours, that changes everything. But if it’s still a half-day of configuration, I need to know that too.
What’s the realistic timeline for someone with zero automation experience to go from blank canvas to a working RAG assistant? And what actually breaks in that process? Is it always the retrieval configuration, or can the generation part be finicky too?
You can go from zero to working in roughly 30-45 minutes with Latenode’s visual builder. Here’s the flow: start with a RAG template or describe what you want in plain English to the AI Copilot, and it generates a workflow for you. You drag a retrieval node and a generation node into a workflow, connect them, point them at your knowledge base, and you’re mostly done.
Configuration is minimal for a proof of concept. You set your knowledge base source, maybe tweak a prompt or two, and test. That’s genuinely it for basic functionality.
The visual builder means you’re not writing code. You’re selecting nodes, configuring their inputs, and connecting them. It’s designed so non-technical people can build this without help. The harder part isn’t the tool—it’s having your knowledge base ready and formatted correctly.
I did this myself last month. First working RAG chatbot: about an hour. That included setting up the knowledge base, configuring retrieval, testing a few queries, and adjusting the generation prompt because initial responses were too terse.
The visual builder is genuinely intuitive. You drag nodes representing retrieval and generation, connect them, and configure them. No code required. The cognitive load is understanding what each node does, not wrestling with syntax.
What took time: formatting my docs correctly so retrieval would work. If your knowledge base is already clean and accessible, you’re looking at 20-30 minutes for the workflow itself. If you need to prep data first, add another 30 minutes.
Breakage points: retrieval pulling wrong chunks is the most common issue, not generation. Generation usually does fine unless your prompt is really poorly written. You’re looking at minimal troubleshooting for a proof of concept.
I tested this with zero prior automation experience. Timeline for basic working RAG workflow: approximately 40 minutes. This included reading documentation, building the workflow, configuring knowledge base connection, and running test queries.
The visual builder significantly reduces friction. Node-based composition is intuitive when learning for the first time. Configuration is straightforward because nodes expose relevant parameters without overwhelming options.
Time allocation: workflow assembly and configuration (15 minutes), knowledge base setup and connection (15 minutes), testing and minor prompt adjustment (10 minutes). The main variable is knowledge base readiness. Clean, accessible docs dramatically reduce the timeline.
Time-to-functional-RAG using visual builders: 25-50 minutes for basic proof of concept assuming prepared source data. Primary time allocation: workflow assembly (10-15 min), knowledge base integration (5-10 min), retrieval-generation tuning (10-25 min).
Visual builders eliminate syntax overhead, reducing cognitive load to node configuration and logical flow. Main variable affecting timeline: data preparation quality. Properly formatted, indexed source documents substantially accelerate setup.
Incidents during development: retrieval configuration typically requires iterative adjustment (ranking, chunk optimization). Generation usually performs acceptably with minimal modification. Success probability of working prototype exceeds 90% within 60-minute window.