This article explains how to improve RAG retrieval quality by combining vector and keyword search via RRF fusion, adding cross-encoder reranking, rewriting user queries for better retrieval, and using HyDE to generate hypothetical answers for embedding-based search, with a practical Java implementation example.
The article details how a 15% recall@K improvement was achieved in a RAG system by building an offline test set and applying six concrete query‑optimization methods—including rewrite, HyDE, multi‑query, query splitting, contextual completion, and keyword enhancement—while discussing their trade‑offs and implementation tips.
Real users ask informal, incomplete questions that often miss the right documents, so the article classifies common failure types, explains three query‑optimization techniques—Query Rewrite, Multi‑Query, and HyDE—provides concrete prompts, code snippets, selection guidelines, evaluation metrics, and practical deployment pitfalls.
The article explains how Hypothetical Document Embeddings (HyDE) improve Retrieval‑Augmented Generation by generating a synthetic answer before vector search, allowing the system to embed richer semantic intent rather than relying on shallow keyword similarity, and provides a step‑by‑step implementation using LangChain.
This article explains why Reciprocal Rank Fusion often outperforms weighted‑sum fusion in Retrieval‑Augmented Generation, presents a three‑layer approach to keep knowledge bases timely, discusses HyDE’s cost‑benefit trade‑offs, and offers concrete interview‑ready answers for common RAG follow‑up questions.
