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.

Long-form question answering (LFQA) demands nuanced evaluation of multi-sentence explanatory responses, yet existing metrics often fail to reflect human judgment. We present LFQA-HP-1M, a large-scale dataset comprising 1.3M human pairwise preference annotations for LFQA. We propose nine rubrics for answer quality evaluation, and show that simple linear models based on these features perform comparably to state-of-the-art LLM evaluators. We further examine transitivity consistency, positional bias, and verbosity biases in LLM evaluators and demonstrate their vulnerability to adversarial perturbations. Overall, this work provides one of the largest public LFQA preference datasets and a rubric-driven framework for transparent and reliable evaluation.