zlacker

>>johnjw+(OP)
Having worked building out a RAG SaaS platform for the past year and having worked on the vendor side of several keyword-based search systems in the past 10 years, I can say it's absolutely necessary to have some kind of hybrid search for most use cases I've seen.

The problem is that most people don't have experience optimizing even 1 of the retrieval systems (vector or keyword), so a lot of users that try to DIY build end up with an awful time trying to get to prod. People are talking about things like RRF (which are needed) but then missing other big-picture things like the mistakes everyone makes when building out a keyword search (not getting the right language rules in place) and also not getting the right vector side (finding the right embedding models, chunking strategies, etc).

I recognize I have a bit of a conflict of interest since I'm at a RAG vendor, but I'll abstain from the name/self-promotion and say: I've seen so many cases where people get this wrong, if you're thinking RAG you really should be hiring a consultant or looking at a complete platform from people that have done it more. Or be prepared to spend a lot of cycles learning and iterating

>>eskiba+nn2
People dramatically underestimate the complexity of even reasonably relevant search systems.

One reason is unlike other data products - it’s an active, conscious action of users. If ads or recommendations are wrong nobody gets mad. But screw up search and it’s like the shop sales person taking you to the wrong aisle. It’s actively frustrating.

So basically every useful search system is disliked to some degree because it will get some things wrong some of the time.