This blueprint operationalizes the Institute for Integrated Transitions (IFIT) report "AI on the Frontline: Evaluating Large Language Models in Real‑World Conflict Resolution" (30 July 2025). It converts the report's three scenarios (Mexico, Sudan, Syria) and ten scoring dimensions into concrete evaluation prompts. The rubrics emphasize professional conflict-advisory best practices: due diligence on context and user goals, results-over-ideology, alternatives to negotiation, trade-offs, risk disclosure, perspective-taking, local-first approaches, accompanying measures, and phased sequencing.

Additionally we have included the system prompt used in a follow-up report titled "Improving AI Conflict Resolution Capacities: A Prompts-Based Evaluation", summarized thus:

Following the release of AI on the Frontline: Evaluating Large Language Models in Real‐World Conflict Resolution—a groundbreaking study by the Institute for Integrated Transitions (IFIT)—new testing has shown that the main weaknesses identified in the original research can be improved through simple adjustments to the prompts used for large language models (LLMs) like ChatGPT, DeepSeek, Grok and others. While today’s leading LLMs are still not ready to provide reliable conflict resolution advice, the path to improvement may be just a few sentences away—inputted either by LLM providers (as “system prompts”) or by LLM users.

This evaluation assesses LLM clinical reasoning and safety awareness in complex, ambiguous cases where errors commonly arise from human cognitive bias, relational dynamics, and system gaps. It moves beyond factual recall to probe whether a model can navigate uncertainty, avoid premature closure and anchoring, and apply practical judgment when data sources conflict or are incomplete.

Scenarios are grounded in real-world cases from AHRQ's Patient Safety Network (PSNet), especially the expert-curated WebM&M series of anonymized medical error narratives. Using PSNet cases anchors rubrics in documented patient-safety events and authoritative commentary, ensuring evaluations are evidence-based rather than hypothetical.

Core Themes Tested:

• Longitudinal synthesis & anchoring bias: valuing a persistent patient narrative and resisting overreliance on point-in-time negative tests.
• Practical wisdom & inter-system gaps: resolving discordant findings and ensuring closed-loop follow-through across referrals and care transitions.
• Rapport, trust, and the unreliable narrator: prioritizing safety when self-report is limited, inconsistent, or concealed; emphasizing alliance-building and means restriction.
• Diagnostic overshadowing & zebra hunting: avoiding attribution to a plausible comorbidity and maintaining a broadened differential, especially in vulnerable and immunosuppressed patients.
• Equity and bias-aware safety: recognizing how stigma, structural racism, and disability-related anchoring/overshadowing drive undertreatment or misattribution, and specifying safeguards to ensure guideline-concordant care.