Free Lesson
Making BlitzRank 15x faster with Jev
60 min
Nov 30, 2026 1:00 PM
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What you'll learn
Replace listwise ranking with pairwise decisions
See how a decision model can compare search results pairwise instead of generating a full ranked list.
Use BlitzRank to turn comparisons into rankings
Learn how tournament graphs combine pairwise relevance judgments into an ordered set of search results.
Batch hundreds of relevance decisions efficiently
See how Jev evaluates many pairwise choices in parallel without making each comparison a separate model call.
Measure search quality, latency, and cost tradeoffs
Compare decision-model ranking against an LLM oracle across real retrieval datasets using nDCG, cost, and latency.
Why this topic matters
LLMs can create high-quality search judgments, but listwise reranking can be slow and expensive. This talk shows how replacing BlitzRank’s LLM oracle with batched Jev pairwise decisions made ranking roughly 15× faster while maintaining—and slightly improving—retrieval quality. You’ll see the architecture, evaluation results, and where further optimization may be possible.
