Developer Builds PDF Chatbot Using RAG Without Fine-Tuning Any AI Model

A developer built a system that allows a large language model to answer questions about PDF documents without requiring any fine-tuning or retraining of the model. The approach uses Retrieval-Augmented Generation (RAG), a technique that retrieves only the relevant sections of a document before passing them to the LLM for a response. The pipeline involves extracting text from a PDF, splitting it into overlapping chunks, converting those chunks into numerical embeddings, and storing them in a vector database for similarity search. When a user poses a question, the system identifies the most relevant chunks and supplies only that context to the model, avoiding the need to process the entire document each time. The developer used Python libraries such as PyMuPDF for text extraction and outlined chunking strategies with configurable size and overlap parameters.