I agree.

> "The real challenge in traditional vector search isn't just poor re-ranking; it's weak initial retrieval. If the first layer of results misses the right signals, no amount of re-sorting will fix it. That's where Superlinked changes the game."

Currently a lot of RAG pipelines use the BM25 algorithm for retrieval, which is very good. You then use an agent to rerank stuff only after you've got your top 5-25 results, which is not that slow or expensive, if you've done a good job with your chunking. Using metadata is also not really a 'new' approach (well, in LLM time at least) - it's more about what metadata you use and how you use them.

If this were true, and initial candidate retrieval were a solved problem, teams where search is revenue aligned wouldn't have teams of very well paid people looking for marginal improvement here.

Treating BM25 as a silver bullet is just as strange as treating vector search as the "true way" to solve retrieval.