Enhancing Regulatory Decision-Making through a Retrieval-Augmented Generation (RAG) Based LLM

The regulatory authority responsible for overseeing industries often faces challenges in accessing historical documents and records. These documents are typically stored in physical formats, making them difficult to retrieve and utilize in decision-making. The deterioration of these records over time, combined with the inefficiency of manual searches, can lead to delays and inaccuracies in regulatory actions. Therefore, there is a need to digitize these documents and develop an AI-powered system to support the retrieval of information, enabling faster and more informed decision-making processes.

The local problem we're aiming to solve revolves around the inefficiencies in accessing historical documents and records, particularly in industries that rely on regulatory oversight, like the tea industry. In our community, the physical storage of decades' worth of important documents, such as regulatory board papers, trade agreements, policy decisions, and meeting minutes, has become a significant barrier to efficient decision-making. These documents, stored in physical archives, are prone to damage from poor storage conditions and are time-consuming to retrieve manually. As a result, regulatory officers struggle to make timely and well-informed decisions, affecting both the governance of the industry and the livelihoods of those dependent on it.

This challenge will address these issues by digitizing the physical archives and creating a system that leverages AI for fast and accurate document retrieval. By doing so, it will:

1. Enhance Decision-Making: The AI-based system will allow regulatory officers to access historical documents quickly, enabling them to make more informed decisions regarding industry policies and practices. This will lead to better governance and improved regulatory oversight, benefiting the entire industry.
2. Preserve Institutional Knowledge: With a digital archive in place, institutional knowledge can be preserved and easily transferred to new officers. This ensures continuity in decision-making and regulatory practices, even as experienced officers retire.
3. Improve Industry Efficiency: By making historical records more accessible, industry stakeholders, including tea plantation owners, exporters, and brokers, will benefit from more transparent and efficient regulations. This will improve product quality, promote fairness in the industry, and enhance the local economy.

• Digitize Physical Archives: Convert all physical records, such as board papers, meeting minutes, and trade agreements, into a secure and accessible digital format to preserve historical documents and ensure they are easy to retrieve.
• Develop an AI-Based Retrieval System: Create a Retrieval-Augmented Generation (RAG) based Large Language Model (LLM) that allows users to quickly access relevant historical documents through a user-friendly, chat-based Q&A system.
• Improve Decision-Making Processes: Enable regulatory officers to make more informed, timely, and accurate decisions by providing quick access to historical data and relevant records, reducing delays in policy enforcement and updates.
• Preserve Institutional Knowledge: Ensure that knowledge from retiring or departing officers is not lost by digitizing past records and making them easily accessible to new officers for training and reference purposes.

1. Scanning and converting physical documents into digital formats.
2. Organizing and preparing the collected data for use in machine learning models.
3. Understanding the trends and patterns within the historical documents.
4. Training a RAG-based Large Language Model to facilitate document retrieval and generation of relevant information.
5. Creating a chat-based Q&A interface to interact with the system for retrieving historical knowledge.
6. Hosting the platform for widespread use across regulatory departments.