1 - Getting Started with RAG (Fundamentals)

Welcome to the exciting world of Retrieval Augmented Generation (RAG)! In this video, we'll delve into the powerful technique that allows large language models (LLMs) like GPT-4 and LaMDA to leverage your own data for more accurate and relevant results. Join us as we explore the core concepts of RAG and build a full-fledged system from scratch. We'll cover everything from loading and chunking documents to embedding techniques and vector databases, ultimately creating an AI that's truly augmented by your unique knowledge base. This series is perfect for anyone interested in generative AI, natural language processing, or building custom AI solutions for their specific needs. Whether you're a developer, data scientist, or simply curious about the latest advancements in AI, this series has something for you! Chapters: 00:00 Introduction to RAG - Understanding the concept and its potential 00:43 LLM Knowledge Cutoff Dates - Why your data is crucial for better AI responses 01:24 Augmenting Your Data - Combining your knowledge with LLM capabilities 02:25 Retrieving Knowledge from Knowledge Stores - How RAG systems access and utilize your data 04:11 Embedding & Vector Databases - Transforming text into vectors and storing them efficiently 05:45 Text Queries and Nearest Neighbors - Finding the most relevant information for user queries 06:33 Post-Processing and Prompt Augmentation - Preparing retrieved data for LLM processing and generating responses Stay tuned for the next video where we'll dive into the coding aspects of building a RAG system! Tags: #RAG #RetrievalAugmentedGeneration #GenerativeAI #LLMs #NLP #AI #Chatbots #QuestionAnswering #VectorDatabases #Embeddings #OpenAI #LaMDA #GPT4 #ChromaDB #Pinecone #Elasticsearch #AIdevelopment #DataScience #MachineLearning