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Better Llama with Retrieval Augmented Generation - RAG

James Briggs via YouTube

Overview

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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.

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

Reviews

4.0 rating, based on 1 Class Central review

Start your review of Better Llama with Retrieval Augmented Generation - RAG

  • Profile image for Deepali Singh
    Deepali Singh
    good 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.

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