Building a RAG-Powered Textbook AI Trained on a Single Citation Book

For this project, I created a Retrieval-Augmented Generation (RAG) system that acts as a dedicated AI assistant for any textbook the user provides. Instead of training a large model from scratch, the idea was to combine the power of modern language models with a custom knowledge base built entirely from the user’s book. When the user uploads or selects a citation textbook, the system preprocesses it by splitting the content into clean, searchable chunks and generating embeddings for each section. These embeddings are stored in a vector database, enabling fast and accurate semantic search.

Whenever the user asks a question, the RAG pipeline retrieves the most relevant pages or paragraphs from the book and feeds them into the language model. This ensures that all answers are grounded strictly in the textbook’s content—making the AI reliable for academic, research, and study use-cases. It can generate summaries, explain complex topics, provide chapter-wise breakdowns, or even cite exact sections, all while staying within the boundaries of the supplied book.

This project helped me explore the full lifecycle of RAG systems—text preprocessing, embedding creation, vector search, context injection, and controlled generation. It also demonstrates a practical way to transform any textbook into an intelligent, personalized study assistant that students can interact with naturally, without worrying about hallucinations or external information.