Automated regulatory reporting system using large language models

Abstract

Computer-implement system and method respond to regulatory queries using a modular orchestration framework that leverages large language models (LLMs). A backend computer system receives a regulatory query from a user and identifies subtasks such as regulation interpretation, task matching, and code explanation. For each subtask, the system retrieves a pre-authored natural language prompt from a prompt library and invokes an LLM hosted remotely to generate an intermediate output. The orchestration engine processes the intermediate outputs to produce a structured, machine-readable response, which is returned to the user device. Subtasks may be executed via specialized agents or services, and the structured response may include regulatory summaries, task metadata, and source code explanations. The architecture enables scalable, context-sensitive use of general-purpose LLMs for compliance-related reasoning, while improving traceability, consistency, and output structure through prompt curation and task orchestration.