RAG Series (13): Query Optimization - Asking Better Questions

Summary of Query Optimization Strategies in RAG Systems

This article explores three query optimization strategies for Retrieval-Augmented Generation (RAG) systems and benchmarks them against a naive baseline. The goal is to improve the quality of retrieved documents by addressing different aspects of query formulation and retrieval.

1. Multi-Query

Description: This strategy involves generating multiple rephrased versions of the original query and retrieving documents based on these variations.

• Use Case: Effective when dealing with large knowledge bases where vocabulary is varied, as it helps in capturing different phrasings that might be used to express the same intent.
• Limitation: On small knowledge bases, redundancy can lead to no significant improvement since the base vector search already achieves near-perfect recall.

2. HyDE (Hypothetical Document Embedding)

Description: This strategy generates a hypothetical answer based on the query and uses its embedding for retrieval instead of the original query.

• Use Case: Best suited when there is a significant semantic gap between the question and the expected answer, especially in technical or specialized domains where precise phrasing matters.
• Improvement: Demonstrates the strongest improvement in ranking quality (context_precision +0.14

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