I benchmarked 5 embedding models across 4 datasets
I benchmarked five embedding models across four NanoBEIR datasets and found that bigger embeddings did not always produce better retrieval.
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I benchmarked five embedding models across four NanoBEIR datasets and found that bigger embeddings did not always produce better retrieval.
Repeated user intents can quietly inflate LLM cost and latency. Semantic caching helps, but production use comes with trade-offs.