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

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