See What Candidate Count Adds to Retrieval Latency

How to Use This Tool

Combine per-document scoring with retrieval and model delays before increasing candidate depth. Calculate RAG pipeline latency from rerank candidates, time per candidate, retrieval latency and generation startup.

The failure Reranker Latency is designed to catch

Retrieving more candidates can improve recall while linearly increasing reranking work and delaying the first visible answer token. The boundary is the job stated in See What Candidate Count Adds to Retrieval Latency; Reranker Latency is not intended to score or transform a different workflow.

The Reranker Latency input contract

The fields used for this specific operation are Candidates reranked, Milliseconds per candidate, Retrieval latency ms, Generation startup ms. Keep the source values beside the Reranker Latency result, because replacing the original would remove the evidence needed to reproduce or reverse the operation.

• For Reranker Latency, Candidates reranked starts at 50 in the worked case; replace that example with the matching source value.
• For Reranker Latency, Milliseconds per candidate starts at 8 in the worked case; replace that example with the matching source value.
• For Reranker Latency, Retrieval latency ms starts at 150 in the worked case; replace that example with the matching source value.
• For Reranker Latency, Generation startup ms starts at 500 in the worked case; replace that example with the matching source value.

Worked result for Reranker Latency

The executable case called Default decision scenario expects out: 1,050.0. Verify that observation before entering real material, and then change one Reranker Latency field at a time so an unexpected direction or formatting change can be traced to a specific input.

Reading the Reranker Latency output

It combines candidates reranked, milliseconds per candidate, retrieval latency ms and generation startup ms into one decision result using the formula explained on the page. Apply that answer only when Candidates reranked, Milliseconds per candidate, Retrieval latency ms, Generation startup ms describe the same scope and format as the worked operation. If the source uses different units, quoting, nesting, timing or account rules, a plausible-looking Reranker Latency output is not sufficient validation.

Assumptions attached to Reranker Latency

• Reranker Latency assumes that all inputs describe the same unit or reporting period unless the field explicitly says otherwise.
• Reranker Latency assumes that the model includes only the four visible inputs and does not infer hidden platform charges.
• Reranker Latency assumes that the page never calls an AI model; token ratios, prices, limits and observed rates are user-supplied planning assumptions.

If one of these Reranker Latency assumptions is false, keep the result as a diagnostic rather than production or decision data, and choose an implementation that explicitly supports the missing rule.

Evidence maintained for Reranker Latency

The recorded reference is NIST — AI Risk Management Framework. Reopen that source when the definition, format, fee or policy behind Reranker Latency changes; private configuration and downstream acceptance still have to be checked in the user's own system.

Where Reranker Latency runs

The named operation executes in browser JavaScript without an ecech calculation API. For Reranker Latency, local execution reduces transmission but does not control browser extensions, device security or the destination where the result is pasted, so sensitive inputs still require the user's normal handling rules.