# How can one use an evaluation metric to compare two RAG systems that might have different strengths (e.g., one retrieves better but the other has a stronger generator)? What composite or multi-dimensional evaluation would you do?

Source: https://zilliz.com/ai-faq/how-can-one-use-an-evaluation-metric-to-compare-two-rag-systems-that-might-have-different-strengths-eg-one-retrieves-better-but-the-other-has-a-stronger-generator-what-composite-or-multidimensional-evaluation-would-you-do
Author: zilliz

To compare two RAG systems with differing strengths in retrieval and generation, use a multi-dimensional evaluation framework that separately measures retrieval and generation performance, then combines them based on task priorities. Here’s how to approach it:

**1. Measure Retrieval and Generation Separately** 
First, evaluate the two components independently. For retrieval, use metrics like **Recall@k** (proportion of relevant documents retrieved in the top *k* results), **Mean Reciprocal Rank (MRR)** (ranking quality of the first relevant document), and **precision** (relevance of retrieved documents). For generation, assess answer quality with **ROUGE-L** (text overlap with ground truth), **BERTScore** (semantic similarity), and **factual consistency** (accuracy of claims against retrieved documents). For example, if System A has higher Recall@5 but System B achieves better BERTScore, this highlights their respective strengths.

**2. Combine Metrics with Weighted Scores or Task-Specific Benchmarks** 
Create a composite score by assigning weights to retrieval and generation metrics based on application needs. For instance, in a fact-checking task, factual consistency might matter more than retrieval breadth, so assign 70% weight to generation metrics. Alternatively, use end-to-end task metrics like **QA accuracy** (correct answers in a benchmark) or **human evaluation scores** (e.g., Likert scales for relevance and correctness). For example, if System A’s strong retrieval leads to 90% QA accuracy but System B’s better generator achieves 85%, the choice depends on whether coverage or answer fluency is prioritized.

**3. Use Multi-Dimensional Reporting for Transparency** 
Present a table or dashboard showing both retrieval and generation metrics side by side. For example: 
- **Retrieval**: Recall@5 (System A: 0.85, System B: 0.72), MRR (A: 0.65, B: 0.55) 
- **Generation**: BERTScore (A: 0.82, B: 0.91), Factual Consistency (A: 0.75, B: 0.88) 
This allows stakeholders to prioritize based on their needs. For instance, a medical chatbot might favor factual consistency over retrieval speed, while a customer support tool might prioritize answer fluency. 

By combining component-level metrics, weighted composite scores, and task-specific benchmarks, you enable a nuanced comparison that accounts for both retrieval and generation trade-offs. This approach avoids oversimplification while providing actionable insights for deployment decisions.