Better Llama with Retrieval Augmented Generation - RAG

This course teaches how to implement Retrieval Augmented Generation (RAG) using a Pinecone vector database, a Llama 2 13B chat model, and Hugging Face and LangChain code. The learning outcomes include keeping Large Language Models (LLMs) up to date, reducing hallucinations, and citing original information sources. The course covers Python prerequisites, creating embeddings, building a vector database, initializing Llama 2, and comparing Llama 2 with RAG Llama 2. The teaching method involves practical hands-on sessions and code implementation. This course is intended for individuals interested in artificial intelligence, natural language processing, and open-source technologies.