I’ve been wrestling with this RAG problem for a few weeks now. Every time our internal docs update, our RAG system starts giving outdated answers. It’s like we’re always playing catch-up.
I started looking into how to actually solve this without rebuilding everything from scratch every time something changes. The real issue isn’t the retrieval part—it’s that once you’ve indexed your documents, they’re kind of… stuck until you manually refresh.
I’ve been reading about how Latenode’s AI Copilot Workflow Generation can pull relevant documents dynamically during the workflow execution, not just at index time. So instead of storing static answers, the workflow retrieves fresh context every time someone asks a question. That’s actually different from how I was thinking about RAG before.
Has anyone here actually implemented this kind of live document retrieval in a workflow? Like, does it actually perform well when you’re pulling documents on every query, or does that become a bottleneck? I’m trying to figure out if this solves the stale data problem or just moves the complexity around.
This is exactly where Latenode shines. You’re right that static indexing is the problem. What you want is a workflow that retrieves documents dynamically each time someone asks a question.
The AI Copilot can actually generate this for you. You just describe what you need: “retrieve my latest support docs and answer this question based on what’s current.” The copilot builds the workflow that does exactly that—no manual indexing needed.
I’ve seen this work with teams that have docs changing frequently. The workflow pulls the latest version of whatever documents are relevant and feeds them directly into your generation step. No stale data because each query is working with current information.
Performance is solid if you set it up right. Most document retrievals happen in milliseconds, so you’re not really creating a bottleneck. It’s cleaner than constantly reindexing.
I ran into this exact issue last year. The temptation is to set up a big batch job that reindexes everything on a schedule, but that just delays the problem. Your answers are still stale between reindex cycles.
What actually worked for us was switching to a retrieval-on-demand approach. Each time someone asks a question, you fetch the relevant documents in that moment. It sounds expensive but it’s not really. Document retrieval is fast, especially if your knowledge base isn’t massive.
The tradeoff is that you need your documents accessible in a way that’s queryable—could be a knowledge base, could be cloud storage, doesn’t really matter as long as the workflow can reach it. Once you have that setup, keeping answers fresh becomes automatic. You’re not fighting the system anymore.
One thing I learned the hard way: statically indexed RAG only works if your information changes slowly. The moment you have documents that update weekly or daily, you start to feel the pain.
Dynamic retrieval fixes this, but you need to think about cost. If you’re running retrieval on every single query, that matters at scale. Though honestly, with Latenode’s access to multiple models, you can optimize which retrieval model you use to balance speed and cost. Some models are lighter weight but work fine for document retrieval.