Query rewriting for RAG: how to improve retrieval accuracy

This post introduces query rewriting as a technique to improve retrieval accuracy in RAG pipelines. It defines query rewriting, explains its importance in addressing user intent mismatches and vocabulary differences, and details its workflow within a RAG system. Common techniques like query expansion, decomposition, paraphrasing, multi-query generation, and step-back prompting are described. The post also outlines implementation steps, including query normalization, LLM-based query generation, document retrieval, and final response generation. Limitations such as over-expansion, semantic drift, increased latency, and evaluation complexity are also discussed.