I’ve been reading about RAG (retrieval-augmented generation) for a while now, and honestly, it sounds great in theory. But I’m curious about real implementations. I’ve started exploring Latenode’s RAG capabilities and noticed they have this built-in document processing and knowledge base integration that seems designed specifically for this.
The thing that’s caught my attention is how they handle context-aware responses. From what I understand, you can connect external documents and have AI agents reference them in real time, which is different from just throwing everything at an LLM and hoping it works.
I’m particularly interested in how the retrieval part actually works in practice. Like, when you set up a knowledge base integration, how does it decide what information to pull? And more importantly, are there common pitfalls people run into when building these workflows?
Has anyone here actually deployed a RAG system in production? I’d love to hear what worked and what didn’t. Especially curious about document processing—does it handle PDFs and unstructured data well, or is there a lot of manual preprocessing involved?