Nine RAG Techniques to Boost Retrieval Quality in Production AI Systems

Retrieval-Augmented Generation (RAG) systems often underperform in production because standard vector search can return irrelevant or incomplete results. A technical guide published on DEV Community outlines nine techniques to improve how information is retrieved, filtered, and presented to large language models. Among the key methods are reranking, which re-evaluates candidate chunks using both the query and document content to surface the most relevant results, and hybrid search, which combines semantic vector search with keyword-based approaches like BM25. Other techniques covered include chunking strategies, multi-query retrieval, parent document retrieval, context compression, HyDE, Self-RAG, and CRAG. Together, these methods address distinct weaknesses across the RAG pipeline to improve the overall accuracy and reliability of AI-generated answers.