Reranking RAG Results When Semantic Similarity Picks the Wrong Chunks

Your retrieval pipeline returns five chunks with cosine similarity scores above 0.85, and the LLM still gives a useless answer. The chunks look related to the query, but they don't actually contain the information the user asked for. Semantic similarity got the neighborhood right but landed on the wrong house.

Reranking is the layer that fixes this. It sits between your vector retrieval step and the LLM prompt, re-scoring each candidate chunk with a more expensive but more accurate model. The result is a smaller, higher-quality context window that dramatically improves answer quality.

What you'll learn

• Why cosine similarity on embedding vectors is a fundamentally weak ranking signal
• How cross-encoder rerankers work and how to drop one into an existing pipeline
• Maximal Marginal Relevance (MMR) for reducing redundant chunks
• Reciprocal Rank Fusion for combining multiple retrieval signals
• Practical pitfalls and when reranking is not the right fix

Prerequisites

This article assumes you already have a working RAG pipeline: documents chunked, embedded, and stored in a vector database. Code examples use Python with sentence-transformers and a generic vector store interface. You don't need a specific LLM or vector DB to follow along.

Why Semantic Similarity Fails as a Ranking Signal

Embedding models are trained to project semantically similar text close together in vector space. That works well for clustering and fuzzy search. It works poorly when the user's query is precise and the relevant chunk is buried beneath several topically adjacent but factually different chunks.

Consider a knowledge base about a software product. The query is