How can the number of retrieved documents (top-k) be chosen to balance vector store load and generation effectiveness, and what experiments would you run to find the sweet spot?

To choose the optimal number of retrieved documents (top-k), you need to balance the computational load on the vector store with the quality of the generated output. A smaller k reduces the vector store’s workload by fetching fewer documents, which speeds up retrieval and lowers costs. However, it risks excluding critical context, leading to incomplete or inaccurate generator responses. Conversely, a larger k improves the generator’s ability to synthesize accurate answers by providing more data but increases latency and resource usage. The ideal k depends on factors like query complexity, dataset size, and acceptable response times. For example, a simple FAQ system might work well with k=3, while a research assistant handling nuanced questions might require k=10 to ensure coverage of diverse sources.

To find the sweet spot, run experiments that measure both system performance and output quality. Start by testing a range of k values (e.g., 3, 5, 10, 15) on a validation dataset. Track metrics like retrieval latency (time to fetch documents), generator accuracy (e.g., via BLEU score or human evaluation), and relevance of outputs to user queries. For instance, if increasing k from 5 to 10 improves answer quality by 15% but doubles retrieval time, evaluate whether the trade-off aligns with your application’s priorities. Additionally, test under realistic load conditions: simulate concurrent users to see how the vector store handles high k during peak traffic. Tools like Locust or custom scripts can emulate user queries to identify bottlenecks. For example, if k=10 causes timeouts during stress testing, a lower k might be necessary despite a slight drop in output quality.

Finally, validate results in production with A/B testing. Deploy different k values to subsets of users and compare outcomes like task success rates, user feedback, and system health metrics (CPU/memory usage). For example, a customer support chatbot might show that k=7 achieves 90% resolution rates without overloading servers, while k=5 leads to more escalations. Continuously monitor and adjust k as data evolves—new document additions or shifts in query patterns may require retesting. For instance, an e-commerce search system might need to increase k during holiday sales to handle broader product queries. The goal is to iteratively refine k based on empirical evidence, ensuring a balance between efficiency and effectiveness tailored to your specific use case.