Toward Optimal Search and Retrieval for RAG

examines how retrieval affects performance in RAG pipelines for QA tasks; conducts experiments using BGE-base and ColBERT retrievers with LLaMA and Mistral, finding that including more gold (relevant) documents improves QA accuracy; finds that using approximate nearest neighbor search with lower recall only minimally impacts performance while potentially improving speed and memory efficiency; reports that adding noisy or irrelevant documents consistently degrades performance, contradicting previous research claims; concludes that optimizing retrieval of gold documents is crucial for RAG performance, and that operating at lower search accuracy levels can be a viable approach for practical applications.