A recent initiative under the U.S. Department of the Air Force–MIT AI Accelerator’s Phantom Program has demonstrated the viability of prompt-based software development for military applications. Led by U.S. Air Force cadet Joshua Lynch and mentored by MIT Lincoln Laboratory’s Laura Niss, the three-month project explored whether non-technical service members could independently build functional AI-driven tools using generative chatbots. Lynch employed a methodology termed vibe-coding, relying exclusively on web-based prompts to direct models including Anthropic’s Claude, OpenAI’s ChatGPT, and Google’s Gemini. His initial objective was to develop a comprehensive battlefield application capable of AI-assisted target recognition, modular intelligence, surveillance, and reconnaissance, autonomous striking, and communication management. Working primarily through standard chat interfaces before transitioning to Google AI Studio, Lynch iteratively refined code outputs over twelve weeks. The project quickly revealed the technical boundaries of current large vision-language models. Lynch frequently encountered issues with AI models lacking hierarchical focus, inadvertently modifying unrelated code segments, and producing insecure implementations. To mitigate these challenges, he learned to decompose complex tasks, maintain precise prompt framing, and continuously steer conversational AI back to core objectives. As development progressed, technological constraints and security protocols necessitated a project rescope. The final prototype, designated ROMAD-AI, was repositioned from an active battlefield tool to a document-processing application capable of analyzing tactical maps and generating mission-planning briefings through a VLM-powered interface. While the prototype lacked the security clearance and comprehensive functionality initially envisioned, the initiative successfully validated AI-assisted rapid prototyping for defense innovation. Niss noted that the technology effectively bridges the gap between domain experts and software engineers, enabling tactical personnel to articulate requirements and visualize solutions without extensive coding training. Lynch’s perception of generative AI evolved significantly throughout the trial, shifting from viewing the systems as fully autonomous developers to recognizing them as iterative tutoring tools with notable accuracy limitations on specialized subjects. The project also highlighted model performance variations, with Claude demonstrating greater stability across consistency and anthropomorphism metrics compared to other platforms. Security and code verification remain critical bottlenecks in AI-assisted development. An incident during the project, where the application inadvertently transmitted sensitive documents to a cloud-based Gemini model rather than processing them locally, underscored the risks of unvetted generative code. Researchers concluded that while prompt-driven generation significantly accelerates initial design phases, it cannot yet replace traditional development pipelines for mission-critical or sensitive applications. The findings reinforce a collaborative framework in which non-technical military personnel leverage AI for rapid ideation and prototyping, while technical experts provide essential security audits, architectural oversight, and production-grade implementation. Sponsored by the Department of the Air Force Artificial Intelligence Accelerator, the study provides a measurable roadmap for integrating generative AI into defense software workflows, emphasizing that human expertise and artificial intelligence will continue to function symbiotically rather than substitutively.