Author(s): Gao Dalie (高達烈)

Originally published on Towards AI.

Businesses nowadays encounter a significant challenge with generative AI: they excel in general knowledge but need help to ask about specific data.

The core of the problem lies in the fact that tools like ChatGPT are trained on widely available information, which doesn’t include a company’s internal documents or industry-specific nuances.

This gap can result in inaccurate outputs, known as AI “hallucinations,” compromising the reliability that businesses need for data-sensitive operations.

Enter RAG pipelines combine retrieval and language generation modules to enhance natural language processing tasks. With RAGAS, you can assess the performance of RAG systems without relying on human annotations, making evaluation cycles faster and more efficient.

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RAGAs stands for Retrieval Augmented Generation Assessment. It is a framework introduced for reference-free evaluation of Retrieval Augmented Generation (RAG) pipelines.

RAGAs provide a way to evaluate the performance of RAG architectures across various dimensions, such as the effectiveness of the retrieval system in identifying relevant context passages, the ability of the language model to… Read the full blog for free on Medium.

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Published via Towards AI