Learn how to enhance Llama 2 using Retrieval Augmented Generation (RAG) in this informative tutorial video. Discover how RAG keeps Large Language Models up-to-date, reduces hallucinations, and enables source citation. Follow along as the instructor builds a RAG pipeline using Pinecone vector database, Llama 2 13B chat model, and Hugging Face and LangChain code. Explore topics such as Python prerequisites, Llama 2 access, RAG fundamentals, creating embeddings with open-source tools, building a Pinecone vector database, initializing Llama 2, and comparing standard Llama 2 with RAG-enhanced Llama 2. Gain practical insights into implementing RAG for improved AI performance and accuracy.
Overview
Syllabus
Retrieval Augmented Generation with Llama 2
Python Prerequisites and Llama 2 Access
Retrieval Augmented Generation 101
Creating Embeddings with Open Source
Building Pinecone Vector DB
Creating Embedding Dataset
Initializing Llama 2
Creating the RAG RetrievalQA Component
Comparing Llama 2 vs RAG Llama 2
Taught by
James Briggs
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Reviews
4.0 rating, based on 1 Class Central review
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Deepali Singhgood for Developers. but helped semi techies too.
could clearly see diff after RAG addition.
lot of business use cases possible.
could clearly see diff after RAG addition.
