Perspective: Query Budgeting for LLM-Augmented Analytics
Abstract
We recommend per-user query budgets and graded fallbacks to keep LLM-augmented analytics within cost and latency targets.
Cite this article
Ramirez, A., King, L., & Hill, A. (2024). Perspective: Query Budgeting for LLM-Augmented Analytics. Research Explorations in Global Knowledge & Technology (REGKT), 3 (12). Retrieved from https://regkt.com/article.php?id=373&slug=perspective-query-budgeting-llm-augmented-analytics