Reranking Makes RAG Dramatically Better #RAG #AI #Shorts

追加: 2026-05-05

[00:00:00]Your best chunks are ranked sixth and ninth. Your language model never reads them. The vector database retriever is fast but approximate. It embeds the query and each document separately and compares vectors. The query and document never interact directly. So ranking is imprecise. The most relevant chunk might be sitting at rank six while the language model only reads the top three.

[00:00:22]The fix is two-stage retrieval. Fetch 20 candidates fast with the vector database. Then run a cross encoder that reads the query in each document together and produces a precise relevance score. Sort by that score and pass only the top four to the language model. The coher ranker does all of this in two lines of lang chain code. Click the related video link below for the full episode. Building a re-ranker from scratch and adding the coher ranker to the full rag pipeline line by line.

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