Building and Deploying a RAG Application: From PDF Processing to Production
Last Updated on October 11, 2025 by Editorial Team
Author(s): Ashutosh Malgaonkar
Originally published on Towards AI.
Overview
I built a Retrieval-Augmented Generation (RAG) system that answers physics questions by retrieving relevant passages from an AP Physics textbook and generating responses using an LLM. The application processes 500 pages of Electricity chapters content, creates vector embeddings, stores them in a FAISS index, and serves answers through a Streamlit interface deployed on Hugging Face Spaces.
The article describes the development and deployment of a Retrieval-Augmented Generation (RAG) application that efficiently answers physics questions using a customized system. It outlines the entire process, starting from document processing and creating embeddings to search retrieval and answer generation, detailing technical choices such as the tech stack used, including Python, FAISS, and Streamlit. Additionally, the article highlights the importance of accurate prompt engineering to ensure reliable answers and discusses performance characteristics observed during testing on Hugging Face Spaces.
Read the full blog for free on Medium.
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