Member-only story
What is Reranking in Retrieval-Augmented Generation (RAG)?
Reranking in Retrieval-Augmented Generation (RAG) refers to the process of reordering or refining a set of initially retrieved documents based on their relevance to a user’s query. This step is crucial for optimizing the quality of the information that the LLM (Large Language Model) will use in generating a response.
Why Reranking is Needed After Initial Retrieval
The initial retrieval process is generally designed to be fast, often prioritizing speed over perfect accuracy. As a result, the first batch of documents retrieved can be broad, including both highly relevant and somewhat tangential information. Reranking addresses this by filtering and reordering these documents according to refined relevance scores, reducing noise and irrelevant information.
Without reranking, the LLM might have to sift through less relevant data, which could lead to less accurate or coherent responses and potentially increase the risk of “hallucinations” (when the model generates information not fully grounded in the provided context). Reranking thus directly improves response quality by ensuring the LLM works with the most contextually appropriate information.
