Comparison
Chunking vs Reranker
Chunking and Reranker are both common AI/LLM terms but cover different ideas. Here is a quick side-by-side.
When you would reach for Chunking
Always — chunking is upstream of every other RAG decision. Spending 2 hours on chunking strategy commonly beats 2 weeks of prompt tuning.
A 50-page PDF split into 200-token chunks with 50-token overlap → ~150 chunks indexed.
When you would reach for Reranker
After chunking is sorted, before model upgrades. Reranking is consistently the best dollar-for-quality RAG investment.
A hybrid-search RAG returns 50 candidates; Cohere Rerank trims to the 5 most relevant; faithfulness score jumps from 0.68 to 0.81.
Frequently asked
What is the difference between Chunking and Reranker?
Chunking: Chunking is the process of splitting source documents into smaller passages before embedding them for retrieval. Chunk size and boundaries control how relevant retrievals will be. Reranker: A reranker is a second-pass scoring model that takes the top-K retrieved candidates and reorders them by joint relevance to the query. Typically a cross-encoder; dramatically improves retrieval precision at low cost.
When should I use Chunking vs Reranker?
Always — chunking is upstream of every other RAG decision. Spending 2 hours on chunking strategy commonly beats 2 weeks of prompt tuning. After chunking is sorted, before model upgrades. Reranking is consistently the best dollar-for-quality RAG investment.
Are Chunking and Reranker the same thing?
No. Chunking is agents & tools; Reranker is agents & tools. They are related but address different parts of the AI stack.