How to Improve LLMs with RAG (Overview + Python Code)
🤝 Your team not maximizing Claude? I run 1:1 and team AI workshops for companies doing $10M+ per year: https://aibuilder.academy/yt/Ylz779Op9Pw In this video, I give a beginner-friendly introduction to retrieval augmented generation (RAG) and show how to use it to improve a fine-tuned model from a previous video in this LLM series. ▶️ Series Playlist: https://www.youtube.com/playlist?list=PLz-ep5RbHosU2hnz5ejezwaYpdMutMVB0 🎥 Fine-tuning with QLoRA: https://youtu.be/XpoKB3usmKc 📰 Read more: https://medium.com/towards-data-science/how-to-improve-llms-with-rag-abdc132f76ac?sk=d8d8ecfb1f6223539a54604c8f93d573 💻 Colab: https://colab.research.google.com/drive/1peJukr-9E1zCo1iAalbgDPJmNMydvQms?usp=sharing 💻 GitHub: https://github.com/ShawhinT/YouTube-Blog/tree/main/LLMs/rag 🤗 Model: https://huggingface.co/shawhin/shawgpt-ft References [1] https://github.com/openai/openai-cookbook/blob/main/examples/Question_answering_using_embeddings.ipynb [2] https://www.youtube.com/watch?v=efbn-3tPI_M [3] https://docs.llamaindex.ai/en/stable/understanding/loading/loading.html [4] https://www.youtube.com/watch?v=Zj5RCweUHIk&list=WL&index=4 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
