Free Lesson
Cross-encoders: An End-to-end Guide
Part of The Agent-Powered Super IC
60 min
Jun 17, 2026 12:00 PM
What you'll learn
What are cross-encoders and their uses?
As a form of ranking classifier, cross encoders are an essential tool for search relevance ranking.
Trade-offs using cross-encoders vs. standard vector search
Compared to regular vector search (with bi-encoders), cross-encoders greatly improve semantic ranking, but at a cost.
An end-to-end walkthrough of using cross-encoders (in Vespa)
Exporting the cross-encoder model (ONNX format), tokenizing doc and query text, & connecting the dots in a rank profile.
Tips and tricks to keep latency and cost in check
Optimization tricks like using multi-stage ranking and running the cross-encoder on a GPU.
Why this topic matters
Cross-encoders usually outperform traditional vector search at relevance ranking, because they can capture deep nuances of the query-to-document relationship. They are usually very expensive to run compute and latency-wise, however. For best performance, you need to keep this computation close to the data, implement cross-encoders
You'll learn from
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