Leveraging LLMs in Enhancing Multilateral Negotiations - Omdena (Dec, 2023-Jan, 2024)
Harnessing the power of AI with the Retrieval Augmented Generation (RAG) technique to streamline decision-making in foreign policy negotiations through efficient document analysis and insights.
NLP & LLMS
Tushar Aggarwal
1 min read
Project Overview
Business Impact
Harnessing the power of AI with the Retrieval Augmented Generation (RAG) technique to streamline decision-making in foreign policy negotiations through efficient document analysis and insights.
The ultimate goal of this project is to develop an AI-powered assistant using the Retrieval Augmented Generation (RAG) technique that can efficiently access, process, and analyze a diverse range of policy documents. This assistant aims to serve as a comprehensive guide for foreign policy experts, enhancing their decision-making capabilities across various policy scenarios and streamlining the negotiation process.
The main goals of this AI Innovation Challenge are:
Data Collection: Accumulate a comprehensive set of policies, treaties, and relevant documents across various domains, including digital rights, climate change, feminist concerns, budgets, and more.
Data Processing: Undertake the task of sanitizing, categorizing, and structuring the amassed data, ensuring it’s primed for efficient AI retrieval and analysis.
Model Integration: Incorporate the Retrieval Augmented Generation (RAG) model into the system (laveraging prompt engineering & vector database). This integration aims to proficiently retrieve and generate insights across multiple policy arenas, enhancing the tool’s analytical capabilities.
Interface Development: Design and deploy a basic user-friendly UI tailored for foreign policy experts. This interface will facilitate easy access to the AI tool’s features and insights.
