Cohere Rerank

Why It Matters

Embedding-based retrieval is fast but imprecise. Vector similarity finds documents that are semantically “close” to your query, but this doesn’t always mean they actually answer the question. Reranking adds a second pass that evaluates true relevance with much higher accuracy.

The key insight: embeddings encode documents independently, then compare vectors. Cross-encoders like Cohere Rerank see the query and document together, enabling direct relevance assessment. This catches nuances that embedding similarity misses.

For AI engineers building RAG systems, reranking is often the single biggest quality improvement. Retrieve a larger candidate set (top 50-100), then rerank to find the truly relevant documents (top 5-10). The LLM sees better context, produces better answers.

How It Works

Reranking operates as a post-retrieval refinement step:

1. Initial Retrieval Your vector database returns the top-k documents by embedding similarity. Cast a wide net and retrieve more candidates than you’ll ultimately use.

2. Rerank Scoring Each query-document pair is fed through the reranking model together. The model outputs a relevance score (0-1) based on how well the document answers the specific query.

3. Re-order Results Sort documents by their rerank scores. The top documents by this new ordering go to your LLM as context.

4. Quality vs Latency Trade-off Reranking adds latency (typically 50-200ms depending on document count and length). Balance candidate pool size against response time requirements.

Implementation Basics

Integrating reranking into your RAG pipeline:

API Usage Cohere’s rerank API takes a query and list of documents, returns scored results. Simple HTTP call or SDK integration.

Hybrid Retrieval Combine with BM25 keyword search for hybrid retrieval. Reranking can normalize scores across different retrieval methods.

Batch Sizing Most rerank APIs accept 100-1000 documents per call. Retrieve enough candidates to give reranking meaningful work, but not so many that latency suffers.

Open-Source Alternatives Models like bge-reranker and ms-marco cross-encoders offer self-hosted options. Cohere’s advantage is ease of use and consistent quality.

When to Use Add reranking when your RAG answers are inconsistent or miss relevant information. It’s especially valuable for ambiguous queries where embedding similarity struggles.

Start without reranking, measure answer quality, then add it if retrieval precision is your bottleneck. Many teams see 10-30% quality improvements from reranking alone.