Learn RAG
Lifelong learner, enthusiastic about changing lives through tech.
Scrimba About this course
Learn how to improve the accuracy and reliability of LLM-based apps by implementing Retrieval-augmented Generation (RAG) using embeddings and a vector database.
What you'll learn
Your next big step in AI engineering
2:58
What are embeddings?
6:13
Set up environment variables
1:34
Create an embedding
5:46
Challenge: Pair text with embedding
4:22
Vector databases
3:00
Set up your vector database
3:13
Store vector embeddings
5:47
Semantic search
4:54
Query embeddings using similarity search
9:53
Create a conversational response using OpenAI
8:13
Chunking text from documents
9:36
Challenge: Split text, get vectors, insert into Supabase
5:39
Error handling
3:00
Query database and manage multiple matches
6:08
AI chatbot proof of concept
6:22
Retrieval-augmented generation (RAG)
1:38
Solo Project: PopChoice
4:32
You made it to the finish line!
1:38
