When evaluating retrievers or vector search configurations for RAG, focus on three key criteria: retrieval accuracy, efficiency, and robustness to query variation. These metrics help determine which configuration better aligns with your application’s needs.
First, prioritize retrieval accuracy. Measure precision at k (how many of the top-k retrieved documents are relevant) and recall at k (how many total relevant documents appear in the top-k results). For example, if your RAG system uses the top 5 documents, precision@5 tells you if those results are trustworthy. Mean Reciprocal Rank (MRR) is also useful—it rewards systems that rank the first relevant document higher (e.g., a correct answer in position 1 is better than position 3). Use labeled test queries with known relevant documents to compute these metrics. If one retriever achieves 80% precision@5 versus another at 60%, the former is likely better for tasks requiring high-quality inputs for the generator.
Second, assess efficiency. Latency (time per query) and throughput (queries processed per second) are critical for real-time applications. For instance, a brute-force exact search might have perfect accuracy but be too slow for user-facing apps, while an approximate method like HNSW sacrifices minimal accuracy for faster results. Resource usage (memory, CPU/GPU) also matters—large indices may not fit in memory or scale cost-effectively. Compare configurations under realistic load: a retriever that takes 50ms with 90% accuracy might be preferable to one taking 200ms with 95% accuracy, depending on your latency budget.
Finally, test robustness. A good retriever should handle diverse query types, ambiguous phrasing, and edge cases. Evaluate performance across query categories (e.g., fact-based, opinion-seeking) and measure consistency via metrics like standard deviation in precision@k across subsets. For example, a configuration might excel on technical queries but fail on colloquial language. Additionally, check if retrieved documents cover diverse aspects of a query (e.g., for “health benefits of exercise,” ensure results address mental, physical, and social impacts). Use clustering or similarity scores between retrieved items to quantify diversity. A robust retriever minimizes “silent failures” where irrelevant results go undetected until the generator produces a flawed answer.
By balancing accuracy, efficiency, and robustness, you can objectively compare configurations and choose the one that best supports your RAG pipeline’s requirements.