When “Just Give Us Benchmarks” Misses the Point

I had a call with a customer who asked whether sparse vector search could help their use case. When I asked for specifics, they explained they wanted to identify documents associated with brands or drugs — even when those terms aren't explicitly mentioned in the query. That’s not a traditional retrieval problem; it’s more aligned with semantic enrichment or entity inference.

They then asked, “Are there any benchmarks on sparse vector performance?” I replied, “Are you looking to evaluate performance or capability?” They emphasized performance was their top priority — but it wasn’t clear why sparse vectors were their focus.

And that’s the core disconnect. Benchmarks like NDCG@10 or recall@k are great for evaluating retrieval systems on well-defined datasets. But they don’t address vague, open-ended goals like tagging content with implied entities. Sparse vector search can help improve recall and interpretability in certain cases, but it doesn’t automatically solve enrichment tasks without additional context or structure.

What’s really needed here is clarity of intent. Are they retrieving, tagging, or classifying? Different goals require different approaches — and benchmarks are only meaningful when tied to a clearly defined outcome.

The next step? Real examples. That’s where insight begins!