Need help with AI? Reach out: https://shawhintalebi.com/'>https://shawhintalebi.com/

In this video, I give a beginnerfriendly introduction to retrieval augmented generation (RAG) and show how to use it to improve a finetuned model from a previous video in this LLM series.

Series Playlist:    • Large Language Models (LLMs)  
Finetuning with QLoRA:    • QLoRA—How to Finetune an LLM on a Si...  

Read more: https://medium.com/towardsdatascien...
Colab: https://colab.research.google.com/dri...
GitHub: https://github.com/ShawhinT/YouTubeB...
Model: https://huggingface.co/shawhin/shawgp...

Resources
[1] https://github.com/openai/openaicook...
[2]    • LlamaIndex Webinar: Building LLM Apps...  
[3] https://docs.llamaindex.ai/en/stable/...
[4]    • LlamaIndex Webinar: Make RAG Producti...  


Book a call: https://calendly.com/shawhintalebi
Homepage: https://shawhintalebi.com/'>https://shawhintalebi.com/

Socials
  / shawhin  
  / shawhintalebi  
  / shawhint  
  / shawhintalebi  

The Data Entrepreneurs
YouTube:    / @thedataentrepreneurs  
Discord:   / discord  
Medium:   / thedata  
Events: https://lu.ma/tde
Newsletter: https://thedataentrepreneurs.ck.pag...

Support ❤
https://www.buymeacoffee.com/shawhint

Intro 0:00
Background 0:53
2 Limitations 1:45
What is RAG? 2:51
How RAG works 5:03
Text Embeddings + Retrieval 5:35
Creating Knowledge Base 7:37
Example Code: Improving YouTube Comment Responder with RAG 9:34
What's next? 20:58