Learn RAG
Learn how to improve the accuracy and reliability of LLM-based apps by implementing Retrieval-augmented Generation (RAG) using embeddings and a vector database.
94 min
Content
About
Your next big step in AI engineering
What are embeddings?
Set up environment variables
Create an embedding
Challenge: Pair text with embedding
Vector databases
Set up your vector database
Store vector embeddings
Semantic search
Query embeddings using similarity search
Create a conversational response using OpenAI
Chunking text from documents
Challenge: Split text, get vectors, insert into Supabase
Error handling
Query database and manage multiple matches
AI chatbot proof of concept
Retrieval-augmented generation (RAG)
Solo Project: PopChoice
You made it to the finish line!
Certificate of Completion