To evaluate a multi-step retrieval RAG system compared to a single-step approach, you must track both intermediate retrieval performance and the interplay between steps, in addition to final answer quality. In a single-step system, you primarily measure the relevance of retrieved documents and the correctness of the final answer. For multi-step systems, you need to assess how each retrieval iteration refines the context, whether intermediate steps correct errors or introduce new ones, and how these cumulative steps impact the final output. This requires granular metrics at each stage (e.g., precision of retrieved documents per step) alongside end-to-end metrics like answer accuracy.
For example, consider a multi-step system that first retrieves broad background documents and then performs a second retrieval focused on specific details. You would measure the relevance of documents in the first step (e.g., 80% precision) and whether the second step successfully narrows to higher-precision results (e.g., 95%). If the final answer is incorrect despite high late-stage retrieval scores, you might discover the first step missed a critical source, propagating errors. In contrast, a single-step system’s evaluation would focus solely on whether its one retrieval captured sufficient context to generate a correct answer, without diagnosing hierarchical failures.
The evaluation framework must also account for efficiency trade-offs. Multi-step systems might achieve higher final accuracy but require monitoring for redundant retrievals or unnecessary complexity. For instance, a three-step process where the second retrieval adds no new information could be optimized. In single-step systems, evaluation focuses on balancing retrieval breadth (to avoid missing key data) with computational cost. Both approaches require end-task metrics like answer correctness, but multi-step systems demand additional instrumentation to validate the value of each intermediate decision, ensuring the added complexity improves results rather than introducing noise.