Whitepaper
The Knowledge² research team details how compact, domain-adapted retrievers paired with a calibrated reranker outperform general-purpose providers across finance, clinical, and protein corpora, all under shared evaluation budgets and identical infrastructure.
We compare domain-tuned retrievers against leading foundation models using identical budgets: Finance and Clinical corpora at top-K=5 (with and without rerankers), UniProtKB at top-K=2, shared GPT-4o-mini generation, and a t3.large EC2 instance for the local models. The study shows that specializing dense retrievers on production queries, coupled with a cross-encoder reranker, consistently yields higher faithfulness and answer relevancy while reducing catastrophic errors.
The paper reinforces recent theory: fixed-dimensional embeddings cannot represent every relevance pattern. Rather than scaling universal models, Knowledge² advocates a specialize-not-just-scale strategy, fine-tuning compact retrievers on real interaction data and using a lightweight reranker to enforce precision.
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