To compare two RAG systems with different strengths—one excelling at retrieval and the other at generation—a multi-dimensional evaluation strategy is needed. This approach should separately assess retrieval and generation performance while also measuring their combined impact on end tasks. By using a mix of automated metrics, task-specific scores, and targeted human evaluation, developers can objectively identify which system performs better for their specific use case.
First, evaluate retrieval and generation components independently. For retrieval, use metrics like recall@k (how many relevant documents are retrieved in the top-k results) and precision@k (how many of the top-k results are relevant). For example, if System A retrieves 8 out of 10 relevant documents (recall@10=0.8) but System B only retrieves 5 (recall@10=0.5), System A has a clear retrieval advantage. For generation, metrics like ROUGE-L (measuring overlap with reference answers) or BERTScore (semantic similarity) can quantify output quality. If System B generates answers with a BERTScore of 0.85 compared to System A’s 0.70, it highlights System B’s stronger generation. Separating these scores clarifies where each system shines.
Next, create a composite metric that combines retrieval and generation performance. Assign weights to each component based on the application’s priorities. For instance, in a fact-checking task, retrieval accuracy might be weighted higher (e.g., 70% retrieval, 30% generation), while a creative writing assistant might prioritize generation (e.g., 30% retrieval, 70% generation). Calculate a weighted score like (recall@k * retrieval_weight) + (BERTScore * generation_weight). Additionally, use end-to-end task metrics like answer correctness (exact match or human-rated accuracy) to measure how well the combined system works. For example, if System A’s strong retrieval leads to more factually correct answers in a QA task, it might outperform System B despite weaker generation.
Finally, conduct scenario-based testing to validate real-world performance. Use a diverse test dataset covering edge cases, ambiguous queries, and domain-specific tasks. Track metrics like latency (response time) and failure rate (how often the system returns “I don’t know” incorrectly). For subjective tasks (e.g., writing product descriptions), include human evaluators to rate outputs for clarity, coherence, and relevance. If System B’s superior generation produces more natural-sounding responses but occasionally hallucinates, human feedback can quantify the trade-off. By combining automated metrics, weighted scores, and human judgment, developers can make informed decisions about which system aligns best with their requirements.
