Retrieval-Augmented Generation RAG rewriter component

Retrieval-Augmented Generation RAG rewriter component is the module that transforms a raw user query into an optimized search string before retrieval begins. Using a light language model or prompt-based template, the rewriter expands acronyms, adds missing context, resolves co-references in multi-turn chats, and removes noise words. The output is a concise, semantically rich query that yields higher-quality vectors or BM25 hits, boosting recall without increasing token cost. Advanced implementations inject domain-specific synonyms, attach mandatory filters (e.g., date, jurisdiction), and tag PII for masking. By feeding cleaner queries to the retriever, the rewriter slashes “no hit” rates, shortens answer latency, and reduces hallucinations in the downstream generation step. Engineers monitor rewriter impact through metrics such as retrieval precision and end-to-end faithfulness, iterating on prompt design or fine-tuning as corpora evolve. In production, the component runs stateless in microservices or as a LangChain chain, making it easy to A/B test alongside new embedding models.