Introduction<\/strong><\/p>\n Talks<\/a>\u00a0to end the war in Ukraine are in motion. As the Trump administration seeks to keep\u00a0its promise<\/a>\u00a0to end the war in Ukraine, negotiators must consider long-standing research on\u00a0war termination<\/a>\u00a0and peace negotiations. Despite calls for a swift\u00a0ceasefire<\/a>, this body of work shows that peace talks tend to take longer than initially anticipated.\u00a0War is an extension of politics<\/a>, and negotiations are shaped by a broad range of factors\u00a0beyond<\/a>\u00a0simply tallying battlefield successes or assessing when combatants will\u00a0exhaust<\/a>\u00a0their resources.<\/p>\n A\u00a0report<\/a>\u00a0from the Center for Strategic and International Studies (CSIS), drawing on data from the\u00a0Uppsala Conflict Data Program<\/a>\u00a0(UCDP), found that 60 percent of all wars conclude through some form of compromise. While wars can end in various ways, a negotiated settlement remains a\u00a0distinct option<\/a>\u00a0for Ukraine. Therefore, identifying the factors that influence the complex process of war termination is essential for understanding how negotiations between the United States, Russia, Ukraine, other key states in Europe, and the broader international community may unfold.<\/p>\n War negotiations are inherently complex, marked by uncertainty, competing interests, and shifting variables. While artificial intelligence (AI) can help navigate this complexity, its effectiveness depends on proper application. Large language models (LLMs) are generalists, but their outputs can be refined using techniques such as\u00a0retrieval-augmented generation<\/a>\u00a0(RAG),\u00a0few-shot learning<\/a>, and\u00a0chain-of-thought<\/a>\u00a0reasoning. The key lies not only in the model itself but in the quality of the data and the sequencing of questions. By curating datasets focused on historical peace negotiations and structuring prompts to reflect real-world decisionmaking, AI moves beyond simple summarization to become a tool for systematically analyzing war termination and the challenging path toward a negotiated settlement in Ukraine.<\/p>\n<\/div>\n<\/div>\n<\/div>\n 60 percent of all wars conclude through some form of compromise.<\/p><\/blockquote>\n<\/figure>\n<\/div>\n<\/div>\n This report leverages AI to explore possible pathways to peace between Russia and Ukraine. Researchers at the CSIS Futures Lab applied foundational literature on peace negotiations in international relations (IR) to curate datasets and refine their approach to prompt engineering, incorporating techniques such as few-shot learning, in-context learning, and chain-of-thought reasoning. The study demonstrates that\u00a0AI techniques like RAG can be effectively used to rapidly understand and summarize key documents related to a potential peace agreement in Ukraine<\/strong>.<\/p>\n The LLM\u2019s outputs provide key recommendations for policymakers and analysts assessing possible pathways to peace. First,\u00a0territorial control and security guarantees will be central to any agreement<\/strong>. Both the AI-driven analysis using RAG and recent\u00a0peace discussions<\/a>\u00a0in Munich and Riyadh highlight the significance of these factors. The findings of this report indicate that lasting peace requires a transparent, fair, and inclusive negotiation process\u2014one that fully engages Ukraine and European states. This stands in contrast to emerging proposals that prioritize negotiations between Moscow and Washington while pressuring Ukraine into\u00a0immediate elections<\/a>\u00a0and\u00a0resource concessions<\/a>. The real challenge isn\u2019t merely bringing parties to the negotiating table but navigating the complex technical details that will shape long-term stability. Key issues include territorial arrangements, security guarantees, demilitarized zones, and verification mechanisms. The most difficult work in diplomacy lies ahead.<\/p>\n Second, negotiators must identify clear trade-offs. Not all peace agreements follow the same blueprint. While historical precedent and AI-driven analysis emphasize the importance of postwar justice, economic recovery, and social welfare in achieving lasting peace, political realities will ultimately determine what is negotiable.\u00a0Russia<\/a>\u00a0has hardened its demands rather than offering concessions, making prosecutions for war crimes\u2014however justified\u2014highly unlikely. Negotiators will have to balance upholding international norms with the imperative of ending the war. One area where leverage remains is economic reparations; if the international community is willing, frozen Russian assets could be\u00a0seized<\/a>\u00a0to aid Ukraine\u2019s reconstruction.<\/p>\n Third,\u00a0a lasting peace won\u2019t be shaped solely by Ukraine and Russia\u2014it will require sustained international involvement<\/strong>. Both historical research and AI-driven analysis confirm that\u00a0third-party mediation, security guarantees, and enforcement mechanisms are essential to durable agreements<\/strong>. This dynamic is already unfolding, with\u00a0Saudi Arabia<\/a>\u00a0emerging as a mediator between the United States and Russia. However, beyond facilitating talks, global actors must assume responsibility for monitoring, verifying, and enforcing postwar stability. The United States, in particular, faces a strategic decision: provide Ukraine with unconditional security support or risk long-term instability by demanding\u00a0economic concessions<\/a>\u00a0in exchange for assistance. How Washington and its allies navigate this moment will not only determine the outcome of the war but also shape the credibility of future peace efforts worldwide.<\/p>\n Finally, generative AI presents a powerful tool for\u00a0foreign policy and national security<\/a>, but it must be used wisely. Policymakers should experiment with AI for brainstorming and\u00a0strategic analysis<\/a>, but always in conjunction with expert judgment. The technology has clear limitations\u2014without proper\u00a0benchmarking<\/a>\u00a0and access to tailored, up-to-date data, LLMs cannot reliably generate accurate insights. To maximize effectiveness, policymakers must customize AI models for specific use cases, ensuring outputs align with real-world conditions and the broader bodies of knowledge that inform policy analysis. Ultimately, AI can process historical data and help structure policy discussions; however, it cannot replace the human decisionmaking that determines war and peace.<\/p>\n The report begins by reviewing IR scholarship on war termination and peace processes, highlighting key drivers and roadblocks to achieving lasting peace. It then examines common techniques for working with generative AI, specifically RAG, to refine the performance of LLMs and improve model output. The third section applies the RAG approach to analyze peace agreement data and generate potential peace deals. The final section presents key takeaways and recommendations.<\/p>\n What Do We Know About War Termination and Peace Processes?<\/strong><\/p>\n How wars start and end are central topics in IR. Scholars have identified numerous factors that influence peace negotiations and the terms of any eventual agreement. A key framework for understanding war termination is\u00a0bargaining theory<\/a>, which suggests that conflicts end when the perceived benefits of peace outweigh the costs of continued fighting. In this context,\u00a0information<\/a>\u00a0plays a critical role, as combatants continually reassess their capabilities relative to those of their adversary. War, therefore, is not merely a failure of diplomacy but rather an extension of negotiations between belligerents in which coercion and force play a role. Accordingly, states are typically conceptualized as being able to rationally assess their position vis-\u00e0-vis a rival because the conflict\u2019s progress provides information about what sort of negotiated settlement reflects\u2014at least in a capabilities-focused\u00a0conception<\/a>\u00a0of bargaining\u2014the broader power differential between the belligerents.<\/p>\n Other dynamics, however, such as\u00a0domestic political factors<\/a>, will likely shape any peace negotiations. Scholars have highlighted how domestic political coalitions influence leaders\u2019 calculations of the domestic political costs of defeat. Furthermore, shifts in domestic political coalitions can accelerate war termination by facilitating more effective reassessments of likely outcomes. However,\u00a0domestic political factions<\/a>\u00a0have been shown to protect their interests in any international agreement, potentially complicating the route to a more \u201crational\u201d negotiation outcome. In the case of a Ukraine-Russia peace deal, domestic \u201cspoilers<\/a>\u201d\u2014groups who seek to sabotage any peace process due to their own interests\u2014could obstruct progress, making a mutually acceptable outcome more difficult to achieve.<\/p>\n Scholarship has also\u00a0demonstrated<\/a>\u00a0that more durable peace agreements often include civil society actors, such as local religious organizations or human rights groups. However, the question of who gets a \u201cseat at the table\u201d in peacemaking is\u00a0influenced<\/a>\u00a0by both practical and normative considerations. Additionally, some form of\u00a0justice<\/a>\u2014whether through post-conflict trials, truth commissions, or reparations\u2014can, in certain cases, contribute to the durability of peace agreements. These factors, however, add further complications to the already challenging task of achieving successful peace negotiations and ensuring their subsequent implementation.<\/p>\n Furthermore, the international community is likely to play a crucial role in conflict termination through mediation efforts.\u00a0Research<\/a>\u00a0indicates that \u201cnon-coercive\u201d third-party mediation, whether conducted by states or international institutions, can play a role in \u201csoftening up\u201d belligerents\u2019 war aims, facilitating a cessation of hostilities. Beyond mediation, the\u00a0international community<\/a>\u00a0can help ensure the long-term viability of peace agreements by providing monitoring and verification of the terms, as well as offering\u00a0security guarantees<\/a>. This role is particularly\u00a0significant<\/a>\u00a0in the case of Ukraine, where international oversight may be necessary to prevent Russia from exploiting any cessation in hostilities to rebuild, rearm, and resume its pursuit of war aims.<\/p>\n Many of the aforementioned factors are likely to shape any negotiated peace settlement between Ukraine and all involved parties. For instance, years of war have provided information into both sides\u2019 combat capabilities; however, uncertainty\u00a0remains<\/a>\u00a0regarding the long-term military commitment of the United States and European states, as well as the financial resources available for Ukraine\u2019s reconstruction. Moreover, as suggested in the literature on peace negotiations, domestic political factions, civil society actors, questions of justice, and the role of the international community could all shape the process. Any peace negotiations will, therefore, be highly complex, with numerous variables at play. To explore ways to creatively navigate this complexity, the CSIS Futures Lab employed AI-enabled tools to analyze historical peace agreements as well as current discussions aimed at ending the Russia-Ukraine war. The research team sought to generate possible parameters for an eventual settlement and to conceptualize different versions of a potential peace agreement.<\/p>\n Applying AI to Analyze War and Peace<\/strong><\/p>\n As part of the CSIS Futures Lab\u2019s ongoing efforts to engage in data-driven analysis and experiment with AI-enabled technology, the research team took a creative approach to addressing the complex question of a potential peace agreement related to Russia\u2019s ongoing invasion of Ukraine. To do so, the Futures Lab leveraged specifically tailored data sources, Scale AI\u2019s\u00a0Donovan<\/a>\u00a0platform (an AI-enabled data analysis tool), and a data query method known as\u00a0RAG<\/a>.<\/p>\n While off-the-shelf LLMs can generate responses or summarize text across a wide range of domains, their accuracy\u2014particularly in \u201cknowledge-intensive tasks\u201d\u2014can be\u00a0inconsistent<\/a>. RAG enhances LLM performance by directing models to leverage specific data sources when generating outputs. This approach\u00a0relies on<\/a>\u00a0two key elements: a \u201cretriever,\u201d which queries relevant documents from a specified data source, and a \u201cgenerator,\u201d which synthesizes and summarizes the retrieved information. By structuring AI outputs in this way, RAG helps mitigate model \u201challucinations<\/a>,\u201d or the tendency of LLMs to produce false or misleading information. The result is a more precise, reliable, and contextually informed response tailored to specific user queries.<\/p>\n RAG is just one of several techniques that can enhance LLM outputs for domain-specific tasks. For example, \u201cfew-shot learning<\/a>\u201d\u2014the practice of providing models with a small example set of demonstrations within a specific context\u2014has been shown to improve model performance. This approach works by incorporating specific data examples and correct labeling within a model prompt. Further\u00a0research<\/a>\u00a0suggests that combining multiple contextual prompts can enhance the effectiveness of in-context few-shot learning. In general, the goal is for models to use these examples to better understand the fundamental structure of a task. Additionally,\u00a0chain-of-thought prompting<\/a>\u2014which involves breaking down each step in a task when prompting models and instructing them to decompose responses into intermediate steps\u2014has\u00a0demonstrated<\/a>\u00a0improvements in model outputs.<\/p>\n<\/div>\n<\/div>\n<\/div>\n RAG enhances LLM performance by directing models to leverage specific data sources when generating outputs.<\/p><\/blockquote>\n<\/figure>\n<\/div>\n<\/div>\n For data, the research team leveraged two primary sources. The first was the full text of peace agreements compiled by the\u00a0UCDP<\/a>. The second was a collection of published articles curated by CSIS scholars, analyzing possible pathways to peace between Russia and Ukraine. These data sources were then uploaded to the Donovan platform to implement the RAG approach using commercially available LLMs. Donovan enabled the research team to better optimize search vectors from RAG across larger datasets\u2014a key capability for employing LLMs to explore complex, high-context research areas such as peace negotiations.<\/p>\n The research team incorporated key elements of chain-of-thought prompting to support step-by-step reasoning. The prompting strategy began by first ensuring that the LLM, consistent with few-shot and in-context learning, properly understood the dataset. Next, the researchers directed the LLM to analyze peace agreements in general before narrowing the focus to Ukraine and the specific factors likely to shape the negotiation process. Finally, the model was tasked with exploring possible peace scenarios. This structured approach ensured a logical progression, establishing key analytical steps before prompting the LLM to generate sample peace agreements. In other words, sequencing the right questions is a deliberate strategy when employing LLMs to support foreign policy decisionmaking. Asking a vague or overly simplistic question yields a correspondingly simplistic response. However, posing a series of logically connected questions, grounded in a broader analytical framework, increases the likelihood of generating a well-calibrated and insightful response.<\/p>\n Key Negotiation Factors and Challenges in the Case of Ukraine and Russia<\/strong><\/p>\n As described above, effectively employing AI to analyze complex, high-context tasks requires both gathering the right data and developing a tailored approach to prompting. When applied to generating possible peace agreements for Ukraine, this process necessitates collecting data on peace agreements in general, as well as ongoing, reputable accounts of negotiations. To that end, the research team created two datasets. The first dataset consisted of 333\u00a0peace agreements<\/a>\u00a0from between 1989 and 2018, compiled by the\u00a0UCDP<\/a>. Each entry contained the full text of the respective treaty. The second dataset, developed in collaboration with Ukrainian researchers, included 79 articles from major news outlets discussing potential peace talks. This effort built on the earlier CSIS Futures Lab series,\u00a0Strategic Headwinds<\/a>, which analyzed key factors shaping negotiations.<\/p>\n The figure below outlines the prompting strategy. First, the CSIS Futures Lab ensured that the model used historical peace agreements as an anchor for analyzing Ukraine. Second, the research team integrated current factors influencing peace negotiations in Ukraine. Third, the questions shifted toward identifying potential spoilers and roadblocks that could obstruct the peace process\u2014an approach designed to mitigate confirmation bias and integrate red teaming techniques. With this contextual foundation, the research team then tasked the model with generating possible scenarios.<\/p>\n The initial prompt asked the model to analyze the peace treaties in the UCDP dataset and identify common negotiation parameters. The model\u2019s output highlights several key considerations that have shaped historical peace agreements. These include establishing a clear agenda and methodology for negotiations on specific issue areas, as well as creating timelines and processes for conducting negotiations. Security guarantees and shared principles\u2014such as territorial integrity\u2014emerge as common factors across agreements. Moreover, the analysis indicates that monitoring and evaluation mechanisms are frequent features in historical peace agreements. Finally, the output suggests that economic factors and social welfare issues, including land access, economic development, and human rights, have played a role in shaping historical peace outcomes.<\/p>\n According to the model\u2019s analysis, historical trends indicate that, over time, peace agreements have increasingly emphasized human rights and economic cooperation, and they tend to be more comprehensive while also involving the international community to a greater extent. These findings align with IR scholarship on war termination and peace agreements, which highlights the importance of security guarantees and addressing considerations such as human rights abuses that may have occurred during a conflict. Additionally,\u00a0studies<\/a>\u00a0have shown that peace negotiations placed greater emphasis on human rights in the first decade of the 2000s compared to the previous decade. However, since 2010, there has been growing pessimism about incorporating human rights stipulations into peace mediation efforts. Consequently, these nuances suggest that a model\u2019s empirical claims should always be validated through ongoing research.<\/p>\n With a better understanding of the general parameters of the historical peace agreements, the research team next prompted the model to summarize the main points of the collected articles discussing possible peace processes between Ukraine and Russia.<\/p>\n The model\u2019s output for this second prompt indicates that security guarantees\u2014particularly Ukraine\u2019s relationship with NATO\u2014will be crucial for maintaining Ukraine\u2019s territorial integrity. However, this could conflict with Russia\u2019s interest in ensuring Ukraine remains neutral. Similarly, territorial disputes, including the status of areas currently occupied by Russian forces, are likely to be central to any negotiation. Additionally, Russia would likely demand assurances regarding sanctions relief. Beyond NATO, Ukraine\u2019s potential membership in international organizations, such as the European Union, is also expected to shape negotiation parameters. Finally, the model\u2019s analysis suggests that ceasefire agreements and monitoring mechanisms will be essential for ensuring the durability of any settlement. As with the first prompt, the model\u2019s output aligns with key considerations identified in IR research on war termination. These include the role of the international community in monitoring agreements and the divergent interests surrounding Ukraine\u2019s postwar status\u2014particularly its aspirations to join NATO and the European Union. In the context of a bargaining model of war termination, such factors are likely to be central points of negotiation.<\/p>\n The research team also wanted to ascertain whether the RAG methodology could help identify potential roadblocks to successful negotiations. To this end, the model was prompted to generate possible obstructions to a peace deal. The model\u2019s output suggests that power imbalances between Ukraine and Russia, as well as divergent views on territorial control in regions such as Crimea, could hinder an agreement. Additionally, while Ukraine is likely to want robust security guarantees and possibly NATO membership, this remains a major sticking point for Russia. These factors highlight the ongoing competing interests and strategic priorities of both states in the war. Model analysis also underscores possible domestic factors, such as public opinion, political factions favoring a more hardline approach, and spoiler groups that could undermine the negotiation process. Furthermore, a lack of trust between the belligerents could reduce the likelihood of an agreement and complicate effective monitoring and verification. Here, the conclusions of IR research are particularly relevant. For example, existing models recognize the role of domestic actors and spoiler groups as potential barriers to achieving a lasting peace. Moreover, the balance of power has long been a central theoretical consideration in assessing outcomes in international affairs.<\/p>\n As a final stage, the research team tested if this approach could generate different versions of peace agreements to assist analysts in thinking through alternative negotiated settlements. To achieve this, the model was prompted to generate three peace agreements, each with distinct parameters. The first agreement was a minimalist agreement, representing the least stringent version of a peace settlement generated by the model. Its key provisions include an immediate ceasefire and the withdrawal of Russian forces from Ukraine. However, it does not provide security guarantees for Ukraine, address competing territorial claims, or consider Ukraine\u2019s potential NATO membership. Additionally, under this version of the deal, the international community would not offer technical assistance, monitoring, or support during the implementation phase.1<\/sup><\/p>\n The second agreement was a moderate agreement, which also includes an immediate ceasefire and the withdrawal of Russian troops from Ukrainian territory. Additionally, it calls for the establishment of a buffer zone, along with international monitoring and verification. Beyond these measures, it introduces limited security guarantees through a nonaggression pact between the belligerents. While Ukraine would regain some of its internationally recognized territory, it would not recover all prewar land. Furthermore, Ukraine would agree to neutrality while expanding economic ties in trade and the energy sector. This agreement envisions a more substantial role for the international community, including the provision of technical assistance and support throughout the implementation process.<\/p>\n Finally, the third agreement was a comprehensive and ambitious agreement. This version incorporates all the components outlined in the previous two agreements. However, it also includes security guarantees that may be unappealing from a U.S.\/European perspective and within the current Ukrainian domestic political landscape. Specifically, it proposes a mutual defense pact between Ukraine and Russia. Moreover, the agreement stipulates Ukrainian-Russian cooperation on economic and security issues, including counterterrorism and nonproliferation efforts. In return, Russia agrees to withdraw all troops from disputed territories and formally recognize Ukrainian sovereignty over its internationally recognized land. Additionally, this version of the deal includes provisions for reconciliation and accountability, addressing human rights violations and war crimes committed during the conflict.<\/p>\n However, even in the above output, the comprehensive agreement produces parameters that some may deem unfavorable to Ukraine. To account for this, the research team prompted the model to generate an alternative deal that aligns more closely with Ukrainian interests.<\/p>\n This version of the agreement closely resembles the comprehensive agreement discussed above, particularly on issues related to Ukrainian territorial claims and addressing human rights violations and war crimes. However, one key difference is that this version does not include a Ukrainian-Russian defense pact. Instead, it grants Ukraine the autonomy to join NATO in the future. There is, however, a slight inconsistency in the agreement\u2019s parameters. In this version, Ukraine agrees to maintain its \u201cnonaligned\u201d status, with the option to join organizations such as NATO at a later stage. It is unclear how agreeing to maintain a nonaligned status is compatible with any eventual plan to join the NATO alliance.<\/p>\n In summary, outputs leveraging RAG highlight several notable points. First, in the general analysis of the UCDP data and Ukrainian peace agreement articles, model outputs identified many agreement parameters consistent with those acknowledged in the IR literature. These include the importance of security guarantees, a role for the international community, and considerations of domestic political factors. These findings suggest that LLMs are capable of recognizing broader trends in data, which can be useful for reflecting core topics of discussion in the field of IR.<\/p>\n<\/div>\n<\/div>\n<\/div>\n The approach of using RAG linked to specified data sources was able to generate alternatives for a peace deal, with each proposal varying in the level of comprehensiveness.<\/p><\/blockquote>\n<\/figure>\n<\/div>\n<\/div>\n Second, in terms of themes across the data specific to a Ukrainian peace deal, model output illustrates that issues of territorial integrity and Ukrainian neutrality emerge as key points of concern. For example, the model identifies that difficulties in negotiations between Ukraine and Russia are likely to center around each country\u2019s competing territorial claims and how territory currently occupied by Russian forces will be divided among the belligerents. Additionally, Ukraine\u2019s relationship with NATO is a point of contention. Model outputs indicate discussions of Russian desires for Ukrainian neutrality within the articles in the dataset. However, due to long-term security concerns, the data suggests that Ukraine would likely prefer to join NATO to help guarantee that Russia will not take advantage of a ceasefire to rearm for a new stage in the conflict down the line.<\/p>\n Third, as instructed, the approach of using RAG linked to specified data sources was able to generate alternatives for a peace deal, with each proposal varying in the level of comprehensiveness. The model-generated output was able to recognize many crucial points of discussion that are likely to shape any real-world negotiations. However, initial output demonstrated inconsistencies in how variations of a peace deal addressed territorial disputes between Ukraine and Russia. This required tweaking initial prompts to achieve improved results and logically consistent treaty parameters.<\/p>\n Moreover, and problematic from a Ukrainian and U.S.\/European strategic viewpoint, the most comprehensive version of a deal generated by the model resulted in a Ukrainian-Russian mutual defense agreement, rather than Ukraine joining NATO. Considering Ukraine\u2019s\u00a0stated desire<\/a>\u00a0to join NATO and concerns that Russia may restart hostilities from a more advantageous position following any pause in the conflict, such an outcome diverges from the preferences of many Western states. Therefore, generating a deal more desirable to Ukraine using RAG required specific prompting to direct the model to create a deal more favorable to Ukrainian interests. In this version of the deal, Ukraine is directed to initially retain its neutrality but has the freedom to join international organizations, such as NATO, in the future.<\/p>\n From AI to Policy and Parameters of a Future Deal<\/strong><\/p>\n This report\u2019s analysis, combining historical research and AI-driven modeling, identifies four core takeaways for policymakers navigating ongoing peace negotiations:<\/p>\n First, the analysis demonstrates that issues of territorial claims between the two belligerents, as well as security guarantees for Ukraine, will be core factors shaping any peace negotiations. In all variations of the deal generated by RAG, these issues emerged as key points of dialogue. Moreover, the findings align with ongoing political developments, as discussions of peace underscore the prominence of these themes. Model analysis of the collected texts indicates significant barriers to Ukraine\u2019s NATO accession. However, this may not be the case for Ukraine attaining post-conflict security guarantees from Europe or the United States. The importance of these factors has been driven home by the recent discussions of a peace deal in both Munich and Saudi Arabia. Within these contexts, European states have\u00a0proposed<\/a>\u00a0stationing forces in Ukraine for peacekeeping purposes, while Russia has demanded a\u00a0retraction<\/a>\u00a0of the 2008 promise to allow Ukraine to join NATO at a future date.<\/p>\n That said, making progress in these areas will require Ukrainian involvement. Notably, Ukrainian President Volodymyr Zelensky\u00a0postponed<\/a>\u00a0a planned trip to Saudi Arabia in hopes of diminishing the legitimacy of the recent talks between the United States and Russia. Policymakers must recognize that Ukraine retains the agency to accept or reject any deal, even amid an increasingly\u00a0precarious<\/a>\u00a0military situation. This report\u2019s analysis of historical peace agreements suggests that establishing a clear agenda and methodology for negotiations is important for achieving success. Future negotiations must acknowledge that this framework needs to include key political actors, such as Ukraine and Europe, to ensure a durable peace. Zelensky himself has\u00a0emphasized<\/a>\u00a0this necessity, stating, \u201cFor the war to end with a reliable and lasting peace, no mistakes must be allowed. This is possible only when negotiations are fair, and guarantees are developed with the participation of all those who are truly capable of providing them.\u201d Excluding Ukraine and Europe from the negotiation process could, therefore, have detrimental consequences. While the Trump administration may perceive direct talks with Moscow as the most expedient path to an agreement, such an approach risks undermining the legitimacy and sustainability of any potential settlement if key players are not involved. The fact is,\u00a0research<\/a>\u00a0indicates that successful peace negotiations should not prioritize expediency at the expense of exclusivity, as agreements that exclude critical parties are less likely to endure.<\/p>\n<\/div>\n<\/div>\n<\/div>\n While not ideal, policymakers must remain mindful of the current political constraints that limit what can realistically be included in a final settlement.<\/p><\/blockquote>\n<\/figure>\n<\/div>\n<\/div>\n Second, in terms of themes across the data specific to a Ukrainian peace deal, model output illustrates that issues of territorial integrity and Ukrainian neutrality emerge as key points of concern. For example, the model identifies that difficulties in negotiations between Ukraine and Russia are likely to center around each country\u2019s competing territorial claims and how territory currently occupied by Russian forces will be divided among the belligerents. Additionally, Ukraine\u2019s relationship with NATO is a point of contention. Model outputs indicate discussions of Russian desires for Ukrainian neutrality within the articles in the dataset. However, due to long-term security concerns, the data suggests that Ukraine would likely prefer to join NATO to help guarantee that Russia will not take advantage of a ceasefire to rearm for a new stage in the conflict down the line.<\/p>\n Third, as instructed, the approach of using RAG linked to specified data sources was able to generate alternatives for a peace deal, with each proposal varying in the level of comprehensiveness. The model-generated output was able to recognize many crucial points of discussion that are likely to shape any real-world negotiations. However, initial output demonstrated inconsistencies in how variations of a peace deal addressed territorial disputes between Ukraine and Russia. This required tweaking initial prompts to achieve improved results and logically consistent treaty parameters.<\/p>\n Moreover, and problematic from a Ukrainian and U.S.\/European strategic viewpoint, the most comprehensive version of a deal generated by the model resulted in a Ukrainian-Russian mutual defense agreement, rather than Ukraine joining NATO. Considering Ukraine\u2019s\u00a0stated desire<\/a>\u00a0to join NATO and concerns that Russia may restart hostilities from a more advantageous position following any pause in the conflict, such an outcome diverges from the preferences of many Western states. Therefore, generating a deal more desirable to Ukraine using RAG required specific prompting to direct the model to create a deal more favorable to Ukrainian interests. In this version of the deal, Ukraine is directed to initially retain its neutrality but has the freedom to join international organizations, such as NATO, in the future.<\/p>\n From AI to Policy and Parameters of a Future Deal<\/strong><\/p>\n This report\u2019s analysis, combining historical research and AI-driven modeling, identifies four core takeaways for policymakers navigating ongoing peace negotiations:<\/p>\n First, the analysis demonstrates that issues of territorial claims between the two belligerents, as well as security guarantees for Ukraine, will be core factors shaping any peace negotiations. In all variations of the deal generated by RAG, these issues emerged as key points of dialogue. Moreover, the findings align with ongoing political developments, as discussions of peace underscore the prominence of these themes. Model analysis of the collected texts indicates significant barriers to Ukraine\u2019s NATO accession. However, this may not be the case for Ukraine attaining post-conflict security guarantees from Europe or the United States. The importance of these factors has been driven home by the recent discussions of a peace deal in both Munich and Saudi Arabia. Within these contexts, European states have\u00a0proposed<\/a>\u00a0stationing forces in Ukraine for peacekeeping purposes, while Russia has demanded a\u00a0retraction<\/a>\u00a0of the 2008 promise to allow Ukraine to join NATO at a future date.<\/p>\n That said, making progress in these areas will require Ukrainian involvement. Notably, Ukrainian President Volodymyr Zelensky\u00a0postponed<\/a>\u00a0a planned trip to Saudi Arabia in hopes of diminishing the legitimacy of the recent talks between the United States and Russia. Policymakers must recognize that Ukraine retains the agency to accept or reject any deal, even amid an increasingly\u00a0precarious<\/a>\u00a0military situation. This report\u2019s analysis of historical peace agreements suggests that establishing a clear agenda and methodology for negotiations is important for achieving success. Future negotiations must acknowledge that this framework needs to include key political actors, such as Ukraine and Europe, to ensure a durable peace. Zelensky himself has\u00a0emphasized<\/a>\u00a0this necessity, stating, \u201cFor the war to end with a reliable and lasting peace, no mistakes must be allowed. This is possible only when negotiations are fair, and guarantees are developed with the participation of all those who are truly capable of providing them.\u201d Excluding Ukraine and Europe from the negotiation process could, therefore, have detrimental consequences. While the Trump administration may perceive direct talks with Moscow as the most expedient path to an agreement, such an approach risks undermining the legitimacy and sustainability of any potential settlement if key players are not involved. The fact is,\u00a0research<\/a>\u00a0indicates that successful peace negotiations should not prioritize expediency at the expense of exclusivity, as agreements that exclude critical parties are less likely to endure.<\/p>\n<\/div>\n<\/div>\n<\/div>\n When applied correctly, AI can summarize historical peace agreements, identify key negotiation patterns, and test alternative settlement scenarios.<\/p><\/blockquote>\n<\/figure>\n<\/div>\n<\/div>\n Fourth, generative AI, particularly techniques for refining LLM outputs like RAG and chain-of-thought prompting, can provide valuable insights for policymakers working toward a Ukraine-Russia peace settlement. When applied correctly, AI can summarize historical peace agreements, identify key negotiation patterns, and test alternative settlement scenarios.<\/strong>\u00a0However, for AI to be effective in shaping the Ukraine peace process, it must be integrated thoughtfully alongside expert judgment and real-time intelligence. Policymakers should consider the following best practices:<\/p>\n Despite its strengths, AI is not a substitute for human negotiation, nor can it fully capture the complexity of war termination in the Russia-Ukraine conflict. The war\u2019s evolving nature presents challenges that AI alone cannot resolve. AI struggles to adapt to rapid shifts in U.S., European, and Ukrainian political dynamics, as models trained on past agreements cannot account for these changes without constant data updates. Additionally, without precise prompting, AI-generated peace scenarios risk defaulting to compromises that align with neither the United States\u2019 nor Ukraine\u2019s national interests. While AI can highlight broad themes like territorial disputes and ceasefire terms, it lacks specificity on implementation details such as monitoring mechanisms, verification protocols, and postwar security guarantees.<\/p>\n These gaps underscore that policymakers must avoid treating AI as a shortcut to diplomacy.\u00a0AI can help structure negotiations, but it can\u2019t replace strategy.<\/strong>\u00a0War termination is ultimately a political process, not a computational exercise. While AI-driven tools like RAG can surface insights from historical agreements and test potential settlement scenarios, peace is achieved through intelligence assessments, strategic trade-offs, and tough diplomatic engagement\u2014not automated outputs alone. This analysis suggests that AI tools do not serve as a replacement for human judgment, expertise, and creativity. Nor can models independently align with the political and strategic goals of their users, nor account for human factors such as\u00a0emotion<\/a>\u00a0or\u00a0individual leadership characteristics<\/a>\u00a0that shape foreign policy outcomes. To make AI useful, policymakers must treat it as a force multiplier, not a decisionmaker. That requires constantly refining prompts, integrating the latest battlefield and political intelligence, and applying expert validation to ensure AI-generated insights align with Ukraine\u2019s security needs and long-term stability. The key isn\u2019t just using AI\u2014it\u2019s using it wisely, critically, and in service of\u00a0strategic analysis<\/a>.<\/p>\n<\/div>\n<\/div>\n<\/div>\n<\/div>\n<\/div>\n<\/div>\n![\"\"](\"https:\/\/www.fie.undef.edu.ar\/ceptm\/wp-content\/uploads\/2025\/03\/250227_Reynolds_Figure1-1024x570.jpg\")
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