TOAdvances in large language models (LLM) have captured the world's imagination. With the release of ChatGPT by OpenAIIn November 2022, previously obscure terms like generative ai entered public discourse. In a short time, the LLMs found a wide Applicability in modern language processing tasks. and even paved the way for ai/research/llm-agents” rel=”noopener ugc nofollow” target=”_blank”>autonomous ai agents. Some call it a watershed moment in technology and draw lofty comparisons to the advent of the Internet or even the invention of the light bulb. Consequently, a large majority of business leaders, software developers, and entrepreneurs are eager to use LLMs to their advantage.
Retrieval augmented generation, or RAG, stands as a fundamental technique shaping the landscape of applied generative ai. A novel concept introduced by Lewis et al in their seminal article Augmented Retrieval Generation for Knowledge-Intensive NLP TasksRAG has quickly become a cornerstone, improving reliability and reliability in the results of large language models.
In this blog post, we will go into detail about evaluating RAG systems. But before that, let's set the context by understanding the need for RAG and getting an overview of the implementation of RAG pipelines.
