SysML diagram generation using prompt engineering and RAG (Retrieval-Augmented Generation) technology for flood rescue scenarios Drone Search and Rescue mission. requires significant improvements in innovation, technical depth, logical structure, and experimental validation. Required Skills Essential Technical Skills: Advanced knowledge of SysML (Systems Modeling Language) and diagram generation Expertise in RAG (Retrieval-Augmented Generation) architecture and implementation Strong background in prompt engineering and LLM optimization Experience with AI/ML model evaluation and experimental design Proficiency in research methodology and technical writing Domain Knowledge: Understanding of systems engineering principles Familiarity with disaster response systems or emergency management (flood rescue context) Knowledge of software engineering methodologies Preferred Qualifications: Master's or PhD in Computer Science, Software Engineering, or related field Published research in AI, NLP, or systems modeling Experience with diagram generation or visual modeling systems Scope of Work 1. Innovation Enhancement (Critical Priority) Develop novel technical contributions: Propose and document new frameworks, algorithms, or improvement strategies beyond existing RAG and prompt engineering methods Identify unique technical innovations: Design custom approaches that go beyond "off-the-shelf" closed-source model usage Create a clear innovation statement: Articulate what makes this research contributory to the field 2. Domain Adaptation Justification (technical report) Strengthen flood rescue scenario alignment: Provide detailed justification for why RAG and prompt engineering are specifically suited for flood rescue SysML generation Document domain-specific customizations: Explain technical adaptations made for emergency response requirements Demonstrate specialized features: Show how the approach addresses unique challenges in disaster response modeling 4. Methodology Expansion (Technical report) Substantially expand technical content: Add detailed algorithm descriptions, architecture diagrams, and implementation specifics Increase workload demonstration: Include more comprehensive technical discussions, ablation studies, or additional experiments Provide rigorous technical depth: Move beyond surface-level descriptions to in-depth technical analysis 5. Experimental Section Restructuring (technical report) Reorganize with clear logical flow: Structure experiments with clear hypotheses, methodologies, results, and analysis sections Improve diagram quality: Either regenerate higher-quality SysML diagrams or provide better examples Strengthen validation: Design experiments that clearly demonstrate the effectiveness of the proposed approach Add quantitative metrics: Include measurable evaluation criteria beyond visual inspection Deliverables technical report with all tracked changes Summary document outlining major changes New/improved SysML diagrams (if applicable, in appropriate format) Supplementary materials (code documentation, additional experimental results if needed)