A Comparative Evaluation of RAG Architectures for Cross-Domain LLM Applications: Design, Implementation, and Assessment

In our latest article, we present a clear and carefully controlled comparison of four major RAG approaches that include sparse retrieval, dense retrieval, hybrid retrieval, and fusion retrieval. Using a preregistered RAGAS evaluation protocol, we measure faithfulness, recall, latency, auditability, and operational behavior across finance and ecommerce workloads where factual precision is essential.

The article explains how each retrieval method operates in practice and what kinds of benefits or limitations practitioners should expect. Sparse retrieval delivers strong auditability and precise grounding. Dense retrieval brings wider semantic coverage for varied queries. Hybrid retrieval provides the most balanced performance across precision recall and speed. Fusion retrieval offers the highest factual completeness by combining multiple evidence paths although this requires more computational time.

Readers will find a clear guide for choosing the right retrieval strategy based on their system requirements whether they prioritize transparency multilingual robustness efficiency or maximum factual accuracy.