BackgroundAfghanistan is highly vulnerable to intense and recurring natural hazards that further risk growth and stability. Since 2000; natural disasters (i.e.; droughts; earthquakes; epidemics; extreme temperature; floods; landslides; storms) have affected close to 19 million people; resulting in 10;656 deaths and US$173.11 million in total damages. Of these hazards; droughts have the most widespread impact and affect a larger population. The 5 significant drought events; occurring in 2000; 2006; 2008; 2011/12; and 2017/18; have affected over 17 million people with estimated damages totaling US$142.05 million. With its diverse topography; isolation of many vulnerable communities; and limited coping mechanisms; hazard events in Afghanistan; regardless of security factors; are ever more likely to turn into disasters with significant humanitarian and economic consequences.The World Bank; through the Improving Livelihood Resilience to Climate Change in Afghanistan PASA (P500817); aims to identify investment opportunities for improving livelihoods through more climate-resilient agriculture and water sectors; including by enhancing proactive decision making to effectively mitigate the adverse impacts of natural hazards on life; livelihoods; and property.Within this PASA; the World Bank; with the technical support of a consulting firm; has recently developed the Afghanistan Drought Decision Support Platform (AF-DDSP). This first of its kind innovative tool brings drought monitoring & seasonal forecasting for Afghanistan; utilizing high resolution remote sensing data to enhance predictive capabilities. The AF-DDSP aims to enable the Bank and other stakeholders to better monitor and forecast the drought conditions in the country and inform the design and prioritization of future operations. The AF-DDSP system is built on a modular server-based architecture designed to support efficient data processing; storage; and access. This includes a Data Processing Server (dedicated to processing Earth Observation data and generating drought bulletins and analytical outputs); a Portal Server (which hosts all web services and APIs that provide access to drought related data through a web-based interface); a Database server (that manages and stores structured data; including geospatial and statistical datasets used by the platform) and a File Server (which handles the storage and retrieval of large files; such as EO data outputs; reports; and other binary assets). As a proof-of-concept; the World Bank seeks to engage a qualified firm to develop and deploy an Artificial Intelligence (AI) conversational agent to enhance the AF-DDSP data accessibility and hence facilitate user experience. ObjectiveThe objective of this assignment is to develop an AI-powered conversational agent; integrated with the AF-DDSP; to enable users to query drought-related information using natural language. The solution will be built using a Retrieval-Augmented Generation (RAG) approach; leveraging an existing Large Language Model (LLM) to provide accurate; context-aware responses grounded in 25+ years of AF-DDSP data.This system aims to enhance accessibility and usability of drought forecasts; historical indices; and analytical insights for both technical and non-technical stakeholders; eliminating the need for specialized database skills.Specific objectives of the assignment:*Design and implement a RAG-based AI system that connects a pre-trained LLM with the AF-DDSP database.*Prepare and index AF-DDSP data; including drought indices; forecasts; and historical patterns; to support semantic search and accurate retrieval.*Develop tailored prompt workflows and question-answer formats for different query types (factual; comparative; and predictive).*Evaluate model performance using defined metrics such as factual accuracy; relevance; and latency.*Deploy the conversational agent (e.g.; chatbot) fully integrated with the AF-DDSP website for real-time interaction.*Monitor system performance and gather user feedback to guide continuous improvement.The objectives detailed above require strong coordination and working closely with the WB Task Team Leader (TTL) and the firm that has developed the AF-DDSP.