Essential Insights
- The article introduces a fast routing system that decides whether a question can be answered deterministically using keyword scores, saving about two seconds by skipping model calls on easy questions.
- It leverages retrieval scores and margins to confidently route simple, templated questions directly to deterministic extractors, reserving costly model calls for complex, ambiguous queries.
- Using a lightweight, score-based signal, the system efficiently reduces inferences, cutting full pipeline latency from over a second to about 0.1 milliseconds for straightforward queries.
- The approach emphasizes that optimizing by calling fewer models and using deterministic indicators is more effective and scalable than simply switching to larger or faster models.
Calling the LLM Less Saves Time and Money
Many enterprise systems rely on large language models for accurate answers. However, calling these models for every question can be slow and expensive. Each question triggers multiple model calls, which add latency and cost. For simple questions, this approach wastes resources. Instead, making decisions before involving the model can cut delays and reduce costs. By routing easy questions directly to quick, deterministic processes, organizations can serve users faster and cheaper.
Using a Signal from Retrieval Scores to Make Smart Decisions
Retrieval stages already score how well a question matches specific document lines. This score indicates whether an answer can be found quickly. For questions like “What is the annual premium?” a high score on one line clearly shows the answer. No further model work is needed. Conversely, when scores are flat or uncertain, the pipeline should trigger the full model call. This signals when reasoning or interpretation is necessary. In this way, the system only asks the model when it genuinely improves accuracy.
Balancing Speed and Accuracy Without Buying Faster Models
The key is to set a confidence threshold based on retrieval scores. A high margin between the top scores means the answer is straightforward. Routing those questions directly to deterministic rules saves roughly two seconds per query. This approach leverages existing signals—like keyword matches—and avoids unnecessary model calls. It makes the pipeline faster, cheaper, and more efficient. By focusing on improving decision signals, organizations can handle common questions quickly, reserving powerful models for complex, ambiguous cases.
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