Building Question-Answering Systems with Python and Hugging Face

How do modern applications find exact answers within massive amounts of text? Building your own question-answering system no longer requires proprietary APIs or massive budgets, thanks to open-source AI tools. This text-only course guides you through the process of deploying functional Q&A systems. You will learn to work with Hugging Face transformers, compare extractive and generative approaches, and manage context limitations to ensure highly accurate answers. What you will learn: Understand the foundational differences between extractive and generative Q&A models; Configure Hugged Face pipelines using clean, modern Python practices; Manage context windows and tokenization to prevent information loss; Implement basic Retrieval-Augmented Generation (RAG) concepts; Evaluate model output using standard NLP metrics; Apply open-source models to text datasets through written coding exercises. The course begins with essential terminology and basic natural language processing concepts before moving into practical implementation. You will read through step-by-step explanations of loading pre-trained models, configuring parameters, and querying documents. This course is designed for beginners and software developers looking to enter the world of open-source AI, with no prior machine learning experience required. Start reading now to build your own private, customizable question-answering systems.