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Building RAG can be easy for a simple example, but it's much more nuanced than you might think when you try to do it at larger scale.

With larger-scale real-world enterprise RAG-based applications, you soon realize the enormous time and effort required to experiment with all these levers to optimize the RAG pipeline: which vector DB to use and how, which embedding model to use, pure vector search or hybrid search, chunking strategies, and on and one...

With Vectara's RAG-as-a-service (www.vectara.com) we try to help address exactly this issue: you get an optimized, high performance, secure and scalable RAG pipeline, so you don't need to go through this massive hyper-parameter tuning exercise. Yes, there are still some very useful levers you can experiment with, but only where it really matters.