Tzolkin GARDUNO ALVARADO
Computer Scientist with a wide experience in Data Analysis, Machine Learning, Statistics, Computer Science, Computational Quantitative Trading, Data Base administration and Digital Signal Pattern Recognition. I am in search of opportunities in which data analysis and information extraction of LargeData Volumes are used for decision making. My experience in computer science applied to finance, medicine and technology development has helped me hold directive posts in which I have had to lead groups of people on tackling technical, financial and administrative challenges.
Accepted Talks:
A retrieval-augmented-generation pipeline to help users query system-provided documentation
The increasing integration of AI into computing workflows demands a re-evaluation of traditional operating system design. In environments like Debian, users are often faced with a vast ecosystem of command-line tools, each accompanied by extensive manual pages (man pages) detailing usage, flags, and parameters. While comprehensive, these documents are frequently dense, verbose, and not well-suited for rapid onboarding or targeted queries. We propose a retrieval-augmented generation (RAG) pipeline to bridge this gap, enabling natural language interaction with system documentation. By combining tokenization, embedding, and dense retrieval with a language generation model, our system allows users to query tool usage in plain language and receive concise, contextually relevant responses. This approach streamlines tool discovery and comprehension, and represents a step toward more intelligent, user-aware operating systems.