Rerank Retrieval Results Using Conversation History
bigaiguy · x · 2026-07-11
This introduces Context-Aware Reranking: retrieval merely fetches candidates, while reranking determines what actually enters the context.
Instead of the common practice of stuffing the top-10 results into the model, this approach uses a lightweight reranker to score chunks based on the entire conversation history, rather than just the current query. Chunks that conflict with the existing context are down-weighted, while those that align with the ongoing context are up-weighted. The post mentions that this method significantly improved both token usage and answer quality.
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