Timely and accurate situation awareness underpins effective decision-making in humanitarian response, conflict monitoring, and peacebuilding. Yet, synthesizing heterogeneous and rapidly evolving information from news, conflict databases, and economic indicators remains labor-intensive and delays critical interventions. We present a Retrieval-Augmented Generation (RAG) system that autonomously produces structured, evidence-backed situation awareness reports by integrating real-time data from GDELT, ACLED, ReliefWeb, and World Bank APIs. To rigorously assess report quality without ground-truth references, we introduce a three-level reference-free evaluation framework combining automated NLP metrics, expert review by United Nations crisis analysts, and scalable LLM-as-a-Judge assessment. In a multi-country study across 15 conflict-prone regions, our system generated coherent, relevant, and actionable reports, with UNDP analysts estimating a ~50% reduction in report preparation time. These findings demonstrate the feasibility of deploying RAG-based systems for peacebuilding and humanitarian operations and provide a reproducible framework for generating and evaluating AI-assisted situational intelligence at scale.

Natural language processing (NLP) now shapes many aspects of our world, yet its potential for positive social impact is underexplored. This paper surveys work in “NLP for Social Good" (NLP4SG) across nine domains relevant to global development and risk agendas, summarizing principal tasks and challenges. We analyze ACL Anthology trends, finding that inclusion and AI harms attract the most research, while domains such as poverty, peacebuilding, and environmental protection remain underexplored. Guided by our review, we outline opportunities for responsible and equitable NLP and conclude with a call for cross-disciplinary partnerships and human-centered approaches to ensure that future NLP technologies advance the public good.