Why You Should Always Use a Reranker When Doing RAG

Retrieval augmented generation (RAG) systems can significantly benefit from incorporating a reranking model alongside the standard vector similarity search to enhance the relevance of retrieved context. While vector similarity search is effective in identifying similar texts using embeddings, it often returns results that are not equally relevant to a given query. A reranking model, trained on extensive datasets, evaluates the relevance of these results by directly comparing the query with potential answers, thus identifying which documents are truly pertinent. For example, in a documentation search system, reranking can distinguish between documents discussing similar topics, such as API keys, but in different contexts, ensuring only the most relevant information reaches the large language model (LLM). Incorporating reranking into the RAG pipeline is straightforward and can be implemented using platforms like Vectorize, which supports both similarity and relevance scoring. This process filters out irrelevant information, optimizing the LLM's performance by delivering precise and contextually appropriate responses.

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