7 Retrieval Metrics for Better RAG Systems

Author(s): Abhinav Kimothi

Originally published on Towards AI.

A Simple Guide to Evaluating Accuracy in Information Retrieval Tasks

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Source: AI Image generated by Author using DallE

Large Language Models, or LLMs, is a generative AI technology that has gained tremendous popularity in the last two years. However, when it comes to using LLMs in real scenarios, we still grapple with the knowledge limitations and hallucinations of the LLMs. Retrieval Augmented Generation, or RAG, addresses these issues by providing the LLM with additional memory and context. In 2024 has emerged to be one of the most popular techniques in the applied generative AI world. In fact, one can assume that no LLM-powered application doesn’t use RAG in one way or the other.

RAG enhances the parametric memory of an LLM by creating access to non-parametric memory (Source: Image by Author)

For RAG to live up to the promise of grounding the LLM responses in data, we need to go beyond the simple implementation of indexing, retrieval, augmentation and generation. However, to improve something, we need to first measure the performance. RAG evaluations help in setting up the baseline of your RAG system performance for you to then improve it.

Building a PoC RAG pipeline is not overtly complex. LangChain and LlamaIndex have… Read the full blog for free on Medium.

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Published via Towards AI