[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-84280-en":3,"doc-seo-84280-105":29,"detail-sidebar-cat-0-en-105":83},{"code":4,"msg":5,"data":6},0,"success",{"doc_id":7,"user_id":8,"nickname":9,"user_avatar":10,"doc_module":4,"category_id":11,"category_name":12,"doc_title":13,"doc_description":14,"doc_content":15,"file_id":16,"file_url":17,"file_type":18,"file_size":19,"view_count":20,"is_deleted":4,"is_public":20,"is_downloadable":20,"audit_status":20,"page_count":21,"language":22,"language_code":23,"site_id":24,"html_lang":23,"table_of_contents":25,"faqs":26,"seo_title":13,"seo_description":14,"update_tm":27,"read_time":28},84280,1374391974564,"Clementine","https://ap-avatar.wpscdn.com/avatar/14000253aa45c000a9e?x-image-process=image/resize,m_fixed,w_180,h_180&k=1779874745381141002",6,"Technology","VectorizationLLM Smart Vectorization Based AI Assistant","VectorizationLLM is a specialized large language model based on Google open-weight LLMs, built to help students master smart vectorization and time/wave vector analysis. It supports learning of piecewise functions, Fourier analysis, and symbolic or numerical differential equation solving in MATLAB for the CTEC 247 course. The assistant provides detailed concept explanations with examples drawn from course materials, using a RAG knowledge base and system-prompt architecture, and returns responses with code, text, and images while avoiding direct answer delivery.","arXiv :2607 .07846v 1 [ cs .AI] 8 Jul 2026  \nVectorizationLLM: Smart Vectorization Based AI  \nAssistant  \nR. Duke  \nDepartment of Electrical & Computer Engineering Technology New York Institute of Technology, Old Westbury, NY, USA  \n[rduke01@nyit.edu](rduke01@nyit.edu)  \nJuly 10, 2026  \nAbstract  \nVectorizationLLM is a specialized Large Language Model based on Google open-weight LLMs. The model is designed to assist students to learn smart vectorization, time/wave vector analysis, piecewise functions, Fourier analysis, and differential equations in MATLAB[1] . The course application is CTEC 247: Applied Computational Analysis II by the Department of Electrical & Computer Engineering Technology at New York Institute of Technology Old Westbury. The LLM model is designed to be an instructive assistant, providing detailed explanations of concepts with examples from in-class notes without providing direct answers to questions. The model is designed with a RAG (Retrieval Augmented Generation) knowledge base and system prompt architecture. Examples in both code, text, and images are provided in the LLM responses.  \nContents  \nIntroduction 3  \nPurpose . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3  \nSystem Overview .............................................. 3  \nRelated Tools 4  \nArchitectural Goals & Constraints 5  \nSystem Architecture 5  \nRAG Architecture Diagram ........................................ 5  \nSystem Prompt Design ........................................... 6  \nRAG Documents . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 8  \nAI Assistant Sample Prompts   9  \nEvaluation Metrics . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 9  \nFuture Work 10  \nSource Material Disclosures 10  \nAccessibility of Source Material ...................................... 10  \nAI & Machine Generated Content Disclosure . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 10  \nConclusion 11  \nAcknowledgments 11  \nReferences 12  \nAppendix: Sample Prompt History 13  \nConversation 01: Study Guide: Course Topics .............................. 13  \nConversation 02: Study Guide: CTEC 243 Review ........................... 14  \nConversation 03: Conceptual Explanation: find Function   15  \nConversation 04: Questionable Prompt: find Function Problem   16  \nConversation 05: Study Guide: Piecewise Continuous Functions . . . . . . . . . . . . . . . . . . . . 18  \nConversation 06: Conceptual Explanation: Waveform Concatenation   19  \nConversation 07: Conceptual Explanation: Square Wave   20  \nConversation 08: Questionable Prompt: Piecewise Wave Problem   22  \nConversation 09: Study Guide: Fourier Analysis . . . . . . . . . . . . . . . . . . . . . . . . . . . . 24  \nConversation 10: Conceptual Explanation: sumFouriers Function   26  \nConversation 11: Conceptual Explanation: 95% Power Harmonic ................... 27  \nConversation 12: Conceptual Explanation: 95% Power Harmonic Algorithm ............. 28  \nConversation 13: Conceptual Explanation: sumPowers vs. sumFouriers Functions   29  \nConversation 14: Conceptual Explanation: dsolve Function   30  \nConversation 15: Extended Theory: Capacitor Voltage with dsolve .................. 32  \nConversation 16: Extended Theory: Inductor Voltage with dsolve ................... 33  \nConversation 17: Extended Theory: Resistor Voltage with dsolve ................... 34  \nConversation 18: Extended Theory: Series Current with dsolve .................... 35  \nConversation 19: Conceptual Explanation: Percent Overshoot with dsolve or ode45   36  \nConversation 20: Visualization: Percent Overshoot Chart Markers . . . . . . . . . . . . . . . . . . 37  \nConversation 21: Conceptual Explanation: Percent Undershoot with dsolve or ode45   38  \nConversation 22: Visualization: Percent Undershoot Chart Markers . . . . . . . . . . . . . . . . . 39  \nConversation 23: Conceptual 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