Stop Guessing and Measure Your RAG System to Drive Real Improvements | by Abhinav Kimothi | Oct, 2024

Key metrics and strategies to raise your retrieval-augmented technology efficiency

Advancements in Giant Language Fashions (LLMs) have captured the creativeness of the world. With the discharge of ChatGPT by OpenAI, in November, 2022, beforehand obscure phrases like Generative AI entered the general public discourse. In a short while LLMs discovered a large applicability in modern language processing tasks and even paved the way in which for autonomous AI agents. Some name it a watershed second in expertise and make lofty comparisons with the arrival of the web and even the invention of the sunshine bulb. Consequently, a overwhelming majority of enterprise leaders, software program builders and entrepreneurs are in scorching pursuit of utilizing LLMs to their benefit.

Retrieval Augmented Era, or RAG, stands as a pivotal method shaping the panorama of the utilized generative AI. A novel idea launched by Lewis et al of their seminal paper Retrieval-Augmented Generation for Knowledge-Intensive NLP Tasks, RAG has swiftly emerged as a cornerstone, enhancing reliability and trustworthiness within the outputs from Giant Language Fashions.

On this weblog submit, we are going to go into the main points of evaluating RAG programs. However earlier than that, allow us to arrange the context by understanding the necessity for RAG and getting an outline of the implementation of RAG pipelines.