Key metrics and techniques to elevate your retrieval-augmented generation performance
Advancements in Large Language Models (LLMs) have captured the imagination of the world. With the release of ChatGPT by OpenAI, in November, 2022, previously obscure terms like Generative AI entered the public discourse. In a short time LLMs found a wide applicability in modern language processing tasks and even paved the way for autonomous AI agents. Some call it a watershed moment in technology and make lofty comparisons with the advent of the internet or even the invention of the light bulb. Consequently, a vast majority of business leaders, software developers and entrepreneurs are in hot pursuit of using LLMs to their advantage.
Retrieval Augmented Generation, or RAG, stands as a pivotal technique shaping the landscape of the applied generative AI. A novel concept introduced by Lewis et al in their seminal paper Retrieval-Augmented Generation for Knowledge-Intensive NLP Tasks, RAG has swiftly emerged as a cornerstone, enhancing reliability and trustworthiness in the outputs from Large Language Models.
In this blog post, we will go into the details of evaluating RAG systems. But before that, let us set up the context by understanding the need for RAG and getting an overview of the implementation of RAG pipelines.