AI Engineering/Phase 19/Lesson 66

Cross-Encoder Reranker

A bi-encoder embeds query and document independently. A cross-encoder concatenates them and reads both at once. The cross-encoder is the smartest reader and the slowest. Used as a second stage on the bi-encoder's top-k, it pays for itself.

Build 1.5 hr Python

Prerequisites: Phase 11 lesson 06 (RAG), Phase 11 lesson 07 (advanced RAG); Phase 19 Track B foundations (lessons 20-29); Phase 19 lesson 65 (hybrid retrieval feeding this stage)

#Learning Objectives

Distinguish a bi-encoder retriever from a cross-encoder reranker by their input shape, parameter count, and per-query cost.
Implement a small cross-encoder from scratch as a transformer block that consumes a packed (query, document) sequence and emits a single relevance scalar.
Wire a two-stage retrieve-then-rerank pipeline: retrieve top-N with a cheap retriever, rerank N to top-K with the cross-encoder, return K.
Measure the latency-vs-quality trade-off on a small fixture corpus and pick the right N for a given latency budget.

Full Access lesson

Keep going. The rest of this lesson is one step away.

Unlock all 503 lessons, video, code, quizzes and every future update. Pay in your own currency with Paystack.

See plans Sign in

The text and code of this lesson are based on AI Engineering from Scratch by Rohit Ghumare, MIT licensed.