What are you using to keep RAG retrieval reliable as the knowledge base grows?

I've been looking at how different teams handle rag systems once the knowledge base becomes much larger and more dynamic.

The vector search itself isn't always the difficult part. Problems seem to appear when documents are constantly updated, similar information exists across multiple sources, and the system needs to consistently retrieve the right context.

We've been exploring different approaches around chunking, retrieval, knowledge-based management, and evaluation. Fastgpt is one of the platforms we've been working with in this area, particularly around building knowledge-based AI applications without having to assemble every layer separately.

For those working with vector search in production, what has made the biggest difference to retrieval quality?

Is it better chunking, embeddings, hybrid retrieval, reranking, metadata filtering, or something else?