Data mining + generative AI for more efficient EM information utilization
Project Vivera
Despite the widespread use of various economic models and analytics tools, the landscape of macroeconomic policy formulation often relies on slow, manual processes that involve navigating vast troves of documentation from organizations like the IMF, OECD, and the World Bank's International Bank for Reconstruction and Development (IBRD). These institutions produce essential guidelines, policies, and data critical to governments and researchers worldwide. However, interpreting this data and translating it into actionable insights remains a time-consuming challenge. By RAG training a large language model on extensive datasets from these organizations. The project's goal is to create an AI capable of quickly synthesizing economic assessments, providing governments, policy advisors, and financial institutions with faster, more precise analyses. This tool would drastically reduce the time spent manually sifting through documentation, enabling more efficient decision-making. While the LLM's capabilities in natural language understanding are central to the project’s vision, Project Vivera also focuses on making macroeconomic advice more accessible, contextually informed, and less heuristically vulnerable.