I’ve been diving into retrieval-augmented generation lately, and I have to say, the concept is simpler than the name makes it sound. Basically, RAG lets you pull information from your own documents in real time and feed it into an AI model to generate answers. It’s not magic—it’s just smart information retrieval plus AI generation working together.
What got me interested was realizing how many companies struggle with this. They have mountains of internal documentation, customer data, or research materials, but their AI systems have no access to any of it. So you end up with generic answers that don’t reflect what your company actually knows.
With Latenode, I started exploring how to build this without writing a ton of code. The platform has built-in RAG capabilities, so you can connect to your knowledge base, set up a retrieval step, and then pipe that context directly into your AI model. The whole thing runs in their visual builder.
I set up a test workflow that pulls from a Google Sheets document (our internal FAQ), retrieves relevant rows based on a query, and then uses Claude to generate a proper answer with that context. Response time was around 1.2 seconds, which is solid for real-time support scenarios.
What I’m curious about though—how are others handling the initial document processing? Are you chunking your data before ingestion, or letting the platform handle that automatically? And what models are you using for retrieval versus generation?
You’re spot on about the setup being simpler than people think. I’ve built a few RAG workflows myself, and the key thing Latenode does well is abstract away the complexity.
For document processing, Latenode handles the chunking for you through its document processing node. You just upload your files, and it intelligently extracts and indexes them. No manual segmentation needed.
For model selection, I typically use GPT-4 or Claude for generation since they handle context really well, but for the retrieval side, you can use cheaper models or even semantic search depending on your needs. Latenode gives you access to 400+ models, so you can mix and match. The execution-based pricing means you’re not locked into per-model subscriptions.
One thing that changed my workflow was using the AI Copilot to generate the initial setup. I just described what I needed—“pull FAQs based on customer questions and generate helpful responses”—and it built out most of the workflow for me. Saved hours of configuration.
Check it out here: https://latenode.com
The document chunking question is really important because it affects retrieval quality. In my experience, Latenode’s automatic processing works well for standard documents like PDFs and text files, but if you have very large datasets or specialized formats, you might want more control.
I’ve had better results when I pre-process data into consistent chunk sizes—around 500 words per chunk works for most use cases. Latenode can work with that, and you get more predictable retrieval behavior.
For model selection, I’d recommend starting with what you’re already comfortable with. If your team knows Claude, use Claude. If you’re deep in the OpenAI ecosystem, stick with GPT. The real win with Latenode is that you can test multiple models without changing your workflow. I tested retrieval with both text-embedding-3-small and a custom model, and the smaller one actually performed better for our use case while costing less.
Document chunking can make or break your RAG performance. I learned this the hard way when I tried to index 50 MB of contract documents at once. The retrieval became unreliable because the context windows were too large and noisy.
What worked better was splitting documents by logical sections first—contract clauses, policy sections, etc.—then letting Latenode handle the fine-grained chunking from there. Latenode’s document processing includes intelligent section detection, which helps.
Regarding models, don’t overthink it initially. Use whatever your team already understands. The platform makes it easy to swap models later if your first choice doesn’t meet latency or accuracy targets. I’ve found that for RAG specifically, the retrieval step is more critical than the generation model. A good retrieval mechanism with an average generator beats mediocre retrieval with a great generator.
RAG quality depends significantly on preprocessing and retrieval strategy. Latenode’s automatic chunking is functional, but production systems benefit from domain-specific tuning. For technical documentation, I’ve had success with overlap-based chunking where consecutive chunks share 20-30% of content to maintain context continuity.
Model selection should reflect your use case constraints. Generation quality matters less than retrieval precision in RAG systems. I recommend using smaller, faster models for retrieval (like text-embedding-3-small) paired with larger models for generation. Latenode’s unified model access simplifies this testing process considerably. Cost optimization comes naturally once you understand your retrieval-to-generation ratio.
chunking depends on your doc type. auto processing works fine for most cases, but bigger datasets need manual preprocessing. use smaller models for retrieval, larger for generation. latenode lets u test both without pain.
Start with Latenode’s auto-chunking, then fine-tune if needed. Mix models: fast retrieval, smart generation.
This topic was automatically closed 24 hours after the last reply. New replies are no longer allowed.