RAG makes LLMs better and equal

What this post added

This post presents research on the effectiveness of RAG at scale, specifically highlighting its impact on LLM performance when integrated with a Pinecone serverless index. It details the experimental setup involving a billion-document dataset derived from Falcon RefinedWeb, question generation using GPT-4-turbo, and evaluation using the RAGAS framework with a focus on the 'faithfulness' metric. The technical implementation involved using Cohere's embedding and reranking models, and indexing data in Pinecone. The research demonstrates that RAG significantly outperforms LLMs relying solely on internal knowledge, and that performance scales logarithmically with data size. It also shows that RAG can democratize SOTA performance across different LLMs.