Free Lesson
Decision Models as Rerankers
60 min
Oct 27, 2026 1:00 PM
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What you'll learn
What a decision model is, and why it fits reranking
You send typed questions and get a calibrated probability per answer. No text is generated, so calls are fast.
Four decision models on one benchmark
Jev, Perplexity, Clef and OpenAI against Cohere, Voyage and Mixedbread, all on the same BEIR shortlists
Why request shape matters as much as the model
Pair, batch or choice can take a model from beating BM25 to losing to it, or raise its bill nearly 30x
Calibrated scores let you prune as well as sort
A probability threshold cuts weak context before your LLM pays for it, if the model’s probabilities hold up
How to choose, and how to build your own
Weigh quality, price, tail latency and calibration, using an open wrapper and the full results
Why this topic matters
Rerankers have been a boutique category, where a few vendors each sell one model and you get nothing to tune. A general-purpose decision model, asked one yes-or-no question per document, lands next to the purpose-built rerankers untuned, at a quarter of Cohere’s price. You write the question yourself, so you can tune the reranker to your own domain instead of buying one off the shelf.
