How Multi-Step Retrieval Impacts Latency Multi-step retrieval increases latency because each retrieval step adds sequential processing time. For example, a system might first retrieve broad documents, then filter them with a second query, and finally validate results with a third step. If each step takes 100ms, three steps would add ~300ms (plus overhead). This linear growth in latency becomes significant in real-time applications like chatbots or search engines, where users expect sub-second responses. The delay also depends on external factors like network speed or database query complexity. While parallel processing might reduce time for independent steps, most multi-step workflows rely on sequential dependencies (e.g., refining a query based on prior results), making parallelization impractical.
Evaluating the Quality-Latency Trade-Off A system must weigh whether the improved accuracy from multi-step retrieval justifies the added latency. For instance, a medical diagnosis tool might prioritize accuracy over speed, accepting higher latency to avoid critical errors. Conversely, a voice assistant might limit steps to maintain responsiveness. Metrics like precision/recall, user satisfaction scores, or task success rates can quantify quality improvements. A/B testing can compare single-step and multi-step versions under varying latency constraints. For example, an e-commerce search engine might test whether a two-step retrieval (broad search + price filtering) increases click-through rates enough to offset a 200ms delay. If users abandon results after 1.5 seconds, the system might cap steps to stay under this threshold.
Strategies to Balance Quality and Latency Systems can dynamically adjust steps based on context. For example:
- Adaptive step selection: Use lightweight models to predict if a query requires multi-step processing. Simple queries (e.g., "weather in Tokyo") skip extra steps, while complex ones (e.g., "compare cloud database pricing") trigger additional retrievals.
- Caching: Store results of multi-step retrievals for frequent queries to avoid reprocessing.
- Fallback mechanisms: If a step exceeds a time budget, return intermediate results with confidence scores.
- Hybrid approaches: Run initial steps on fast, approximate indexes (e.g., BM25) and later steps on slower but precise models (e.g., neural rerankers).
By combining these strategies, systems can optimize for both quality and latency, ensuring extra steps are only used when they provide measurable value.