Ai-assisted natural language request transformation with reduced cognitive load

Abstract

Strategic actions can be performed before and during runtime to reduce the cognitive load of an LLM during transformation of a natural language request into a refined structured request. During a development stage, object-type-specific examples are collected; a given example includes an example natural language request and a corresponding example structured request representing a desired LLM output for the example natural language request. The examples can include function calls to train the LLM to include function calls in its output to delegate certain sub-tasks. At runtime, a prompt generation module generates a prompt for a natural language request which includes the natural language request, one or more of the examples, and a data model. The prompt is input to the LLM which produces a preliminary structured output including one or more function calls. The post-processing module executes the function(s) and generates a refined structured output based on the execution results.