Ever wished you could instantly find answers in your pile of PDFs without relying on cloud services? In this episode, we dive deep into Retrieval-Augmented Generation (RAG) - the technology that's revolutionizing how we interact with documents.

Join us as we break down how to build a powerful PDF question-answering system using Python and local Large Language Models. From parsing documents to creating intelligent search capabilities, we'll explore:

• How to transform PDFs into searchable knowledge bases
• The magic of vector databases with ChromaDB
• Techniques for updating your document database without starting from scratch
• Real-world testing strategies to ensure accurate responses
• Building a system that runs entirely on your local machine

Using board games as our playground, we'll demonstrate how this system can instantly answer questions like "How do I build a hotel in Monopoly?" directly from instruction manuals. Whether you're a developer looking to implement RAG or just curious about AI document processing, this episode offers practical insights into making documents come alive with AI.