Using large language models to infer intents of applications

Abstract

Techniques for an intent-inference system to use large language models (LLMs) to infer intents of applications running in user accounts. The system is granted permissions to obtain metadata for application resources, and uses the LLMs to analyze the metadata and infer the intended functionality of the applications. The system uses prompt engineering to interact with the LLMs by embedding the metadata as input into the LLMs, and has LLMs infer the intended functionality of resources in an application. The system generates a formal specification of the application that defines the intended relationships between the resources, and the overall functionality of the application. The formal specification may be used to generate a human-readable summary for users to validate that the inferred formal specification matches their understanding of their application. Further, the intent-inference system may generate a machine-readable representation of the formal specification that is used for various use cases.