#python

This project teaches you how to build a real-world AI research assistant that automatically finds, reads, and answers questions about academic papers using a technique called Retrieval-Augmented Generation (RAG)[1][2][3]. RAG works by first searching for the most relevant information from a large collection of documents, then using a language model to generate clear, accurate answers based on that information—this means you get answers that are up-to-date and grounded in real sources, not just what the AI remembers from its training[1][2][3]. The course is hands-on: each week, you add a new piece, starting with setting up the technical infrastructure, then building automated data pipelines to fetch and process papers, adding powerful search tools (first with keywords, then with AI-powered semantic search), and finally connecting everything to a local AI model that can chat with you and explain complex topics in simple language. By the end, you’ll have a working system you can use to quickly find and understand research papers, and you’ll gain the skills to build similar AI tools for any field—all while learning the best practices used by professional engineers. The main benefit is that you get practical, production-ready AI skills and a tool that makes research faster and more reliable, with answers you can trust because they come directly from the latest papers.

https://github.com/jamwithai/arxiv-paper-curator