Adding a second-stage retriever (e.g., broad recall followed by re-ranking) often improves retrieval quality compared to a single-stage system, but the trade-offs depend on the use case and available resources. A two-stage approach separates the tasks of maximizing recall (finding as many relevant candidates as possible) and precision (ranking the most relevant results first). This division allows each stage to specialize: the first stage uses fast, lightweight methods to gather a large candidate pool, while the second applies computationally expensive models (like cross-encoders) to refine the results. In contrast, a single-stage retriever must balance recall and precision in one step, which can lead to compromises in model design or parameter tuning.
The primary benefit of a two-stage system is improved accuracy, especially in scenarios where precision is critical. For example, in a question-answering system, the first retriever might use BM25 or a dense vector model like DPR to fetch 100 documents, ensuring no relevant answers are missed. The second stage could then apply a BERT-based re-ranker to analyze semantic relationships between the query and each document, boosting the most relevant results to the top. This approach often outperforms a single-stage model because re-rankers can evaluate smaller candidate sets with deeper context analysis. However, the computational cost increases—re-ranking 100 documents per query is feasible, but scaling this to thousands of queries per second requires significant infrastructure.
A single-stage retriever with well-tuned parameters can be sufficient for simpler applications or resource-constrained environments. For instance, tuning a vector search model’s parameters (e.g., chunk size, embedding dimensions, or similarity metric) might achieve adequate results without the complexity of maintaining two systems. If latency is a priority—such as in real-time chat applications—a single-stage approach avoids the overhead of sequential processing. However, single-stage systems struggle when recall and precision require conflicting optimizations. A model tuned for high recall might return too many irrelevant results, while one tuned for precision might miss valid candidates. In such cases, a two-stage system provides a clearer separation of concerns, letting each component excel at its specific task. The choice ultimately hinges on balancing accuracy needs, latency tolerance, and infrastructure capabilities.
