  In this preliminary study, we investigate a GPT-driven intent-based reasoning
approach to streamline tool selection for large language models (LLMs) aimed at
system efficiency. By identifying the intent behind user prompts at runtime, we
narrow down the API toolset required for task execution, reducing token
consumption by up to 24.6\%. Early results on a real-world, massively parallel
Copilot platform with over 100 GPT-4-Turbo nodes show cost reductions and
potential towards improving LLM-based system efficiency.
