How AI Finds the Right Information: Embeddings & Retrieval Explained (RAG Basics)

Description

Understanding how AI systems find the right information is key when working with modern LLM applications. Github: https://github.com/JeffreyRed/LLMOps In this video, I explain the two-stage process used in most AI knowledge search systems: 1️⃣ Embedding documents into vectors 2️⃣ Retrieving the most relevant information using similarity search You'll learn how systems used in RAG (Retrieval-Augmented Generation) pipelines work behind the scenes. We cover: • How articles are split into chunks • What embeddings are and why they matter • How vector databases store knowledge • How similarity search retrieves relevant documents • How LLMs use retrieved context to generate answers This is the architecture used by many AI applications such as: • ChatGPT plugins • AI assistants • Enterprise knowledge search • Documentation bots I also discuss key engineering considerations, including chunking strategies, hybrid search, and maintaining vector indexes. This explanation is useful if you're: ✅ Preparing for AI / ML interviews ✅ Building RAG applications ✅ Learning how LLM systems work internally #AI #LLM #RAG #MachineLearning #ArtificialIntelligence #VectorDatabase #Embeddings #SemanticSearch #AIEngineering #ChatGPT