VectorizationLLM: Smart Vectorization Based AI Assistant
Quick Answer
VectorizationLLM is a specialized AI assistant built on Google’s open-weight LLMs, aimed at teaching smart vectorization and advanced mathematical concepts in MATLAB for CTEC 247 at NYIT.
Quick Take
It utilizes a Retrieval Augmented Generation architecture to provide detailed explanations and examples without directly answering questions.
Key Points
- Designed for CTEC 247: Applied Computational Analysis II at NYIT.
- Focuses on smart vectorization, Fourier analysis, and differential equations.
- Employs Retrieval Augmented Generation for enhanced learning.
- Provides examples in code, text, and images for better understanding.
- Does not provide direct answers, fostering independent learning.
Paper Resources
📖 Reader Mode
~2 min readAbstract:VectorizationLLM 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. 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.
| Comments: | 44 pages, 6 figures |
| Subjects: | Artificial Intelligence (cs.AI); Computers and Society (cs.CY) |
| Cite as: | arXiv:2607.07846 [cs.AI] |
| (or arXiv:2607.07846v1 [cs.AI] for this version) | |
| https://doi.org/10.48550/arXiv.2607.07846 arXiv-issued DOI via DataCite (pending registration) |
Submission history
From: Ryan Duke [view email]
[v1]
Wed, 8 Jul 2026 18:27:39 UTC (4,584 KB)
— Originally published at arxiv.org
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