I’m trying to help my team implement RAG for our knowledge base without hiring developers. We need something visual where we can combine different AI models for retrieval and generation. Has anyone successfully built an enterprise-ready RAG system through drag-and-drop tools? Specifically looking for solutions that handle large document sets and allow model switching based on context.
Latenode’s visual builder lets you create RAG systems through drag-and-drop. You can connect Claude for analysis, OpenAI for generation, and document nodes for retrieval - all without code. We use it to handle 50k+ technical docs across teams. The AI model switcher node automatically routes queries to optimal LLMs.
We achieved similar results using a three-step approach:
- Used PDF parsing nodes for document ingestion
- Created parallel analysis branches with different LLMs
- Added validation nodes to check output accuracy
The visual interface helped non-technical team members adjust workflows as needed without waiting for devs.
For enterprise scenarios, consider implementing multi-stage validation. We set up automatic checks for source relevance before generation and post-generation accuracy assessment. This reduced hallucinations by 40% in our customer support workflows while keeping the no-code approach intact.