How do I implement a 'Corrective RAG' workflow using LlamaIndex and a Reranker?

All answers to this question.

Implementing a reranker in LlamaIndex is quite straightforward using the NodePostprocessor module. I highly recommend the Cohere Reranker; even though it’s an API call, the precision boost is massive. You essentially retrieve the top 20 chunks via vector search (high recall) and then let the Reranker narrow it down to the top 3 (high precision). If you need to stay local for privacy, the BGE-Reranker-v2-m3 is a solid alternative you can run on your own hardware. This "two-stage" retrieval is the standard way to build "Corrective RAG" or CRAG systems that are reliable enough for customer-facing bots.

   Answered 2025-09-20 by Beverly Morris

Does adding a reranker significantly increase the 'Time to First Token' for the user, or is the latency hit negligible compared to the accuracy gain?

   Answered 2025-09-21 by Paul Henderson

• Paul, it adds about 200-500ms depending on the model, but it’s a trade-off most developers are willing to make. To mitigate this, you can stream the retrieval step or use an asynchronous "Postprocessor". In my testing, the accuracy jump from 65% to 88% in factual correctness completely outweighs the half-second delay. Users prefer a slightly slower answer that is actually correct over a fast hallucination any day!

     Commented 2025-09-22 by Kenneth Barnes

Don't forget about the "SimilarityPostprocessor" too. It’s a simple way to filter out any chunks that fall below a 0.7 confidence score before they even hit the reranker.

   Answered 2025-09-23 by Carol Peterson

• Great tip, Carol. Combining a hard similarity threshold with a cross-encoder reranker is the "gold standard" for enterprise-grade retrieval-augmented generation right now.

     Commented 2025-09-24 by Kimberly Foster