RAG Rerank: the Highest-Leverage Upgrade to Your Retrieval Pipeline

Short summary

Reranking—using cross-encoders to re-score top retrieval candidates—fixes RAG's tendency to surface irrelevant documents by word overlap alone. It trades latency and cost for accuracy and is easy to add via services like Cohere Rerank or a simple LLM scoring pass. For applications where wrong answers are costly, reranking is the highest-leverage accuracy upgrade.

• •Basic RAG embeddings rank docs by word overlap, missing truly relevant passages
• •Cross-encoder reranking re-scores top candidates with full context for precise relevance
• •Easy to implement—retrieve wide shortlist cheaply, rerank top 25 with cross-encoder, keep best 2–4

Generated with AI, which can make mistakes.