To choose the optimal number of retrieved documents (top-(k)), you need to balance the computational load on the vector store (e.g., latency, resource usage) and the quality of the downstream task (e.g., answer accuracy). A smaller (k) reduces the vector store’s workload by querying fewer embeddings but risks missing relevant context, while a larger (k) increases the chance of including critical information but adds overhead. The ideal (k) depends on factors like query complexity, data distribution (e.g., dense vs. sparse relevance), and the generative model’s ability to process context. For example, a QA system might require more documents for ambiguous queries, while a chatbot might prioritize speed with smaller (k). The generative model’s context window size also imposes a practical upper limit.
To find the sweet spot, run experiments that incrementally test (k) values while measuring performance and resource metrics. Start with a baseline (e.g., (k=5)) and compare recall@(k) (percentage of relevant documents retrieved) and downstream task accuracy (e.g., answer correctness) against higher/lower (k). For example, test (k=3, 5, 10, 20) on a validation set of queries, tracking metrics like precision (are top results relevant?), mean reciprocal rank (MRR) of correct answers, and generation quality (e.g., BLEU or ROUGE scores for text tasks). Simultaneously, measure vector store latency, CPU/memory usage, and throughput at each (k). The goal is to identify the point where increasing (k) yields diminishing returns in accuracy but significantly impacts latency or resource consumption. For instance, if (k=10) achieves 90% recall but (k=15) only improves to 92% while doubling query time, (k=10) is likely better.
Finally, validate findings with real-world load testing. Simulate production-scale traffic to assess how the chosen (k) affects the system under peak load. For dynamic use cases, consider implementing adaptive (k)—for example, using query classification to adjust (k) based on complexity (e.g., higher (k) for ambiguous or multi-hop questions). Continuously monitor metrics like error rates and user feedback to refine (k) over time. Tools like A/B testing (comparing different (k) values in parallel) or canary deployments can help iterate safely. The “sweet spot” is ultimately a trade-off: prioritize the smallest (k) that maintains acceptable task performance without overloading infrastructure.