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$\ell_{\mathrm{rerank}}$

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where k is the number of candidates handed to the reranker and $\ell_{\mathrm{rerank}}$ is the marginal latency per candidate scored. Recall generally improves as k grows, because a larger candidate set is more likely to contain the passage that actually answers the query — but k multiplies the one latency term a team most directly controls, so it is not a value to leave at a copied default. It is a dial to be set from a measured recall-versus- k curve against an explicit latency budget, the same way the hybrid weight $\alpha$ is set from a measured recall curve rather than assumed.

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\ell_{\mathrm{rerank}}

Equation 13 · AI Agents & Systems

Building Production RAG: An Advanced Technical Guide

This mathematical expression combines the displayed quantities; its precise role follows from the surrounding article text.

where k is the number of candidates handed to the reranker and $\ell_{\mathrm{rerank}}$ is the marginal latency per candidate scored. Recall generally improves as k grows, because a larger candidate set is more likely to contain the passage that actually answers the query — but k multiplies the one latency term a team most directly controls, so it is not a value to leave at a copied default. It is a dial to be set from a measured recall-versus- k curve against an explicit latency budget, the same way the hybrid weight $\alpha$ is set from a measured recall curve rather than assumed.

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