What is RAG?

Discover how RAG is transforming the AI landscape and why it matters for our future

The world of Artificial Intelligence (AI) has witnessed a boom in large language models (LLMs). These powerful tools have impressed us with their ability to generate human-quality text. However, a lingering challenge remains: factual accuracy and real-world knowledge. LLMs can sometimes produce creative, but not entirely truthful, outputs.

This is where Retrieval-Augmented Generation (RAG) steps in as a game-changer. RAG tackles these shortcomings head-on, aiming to significantly improve the quality and relevance of AI-generated text. In the following sections, we’ll delve deeper into the core motivations behind RAG and explore how it benefits various roles in the AI landscape, including ML engineers, data scientists, and generative AI engineers.

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Motivation: Why RAG?

LLMs have achieved impressive feats in text generation. However, they often struggle with limitations in factual accuracy and knowledge about the real world. RAG emerges as a response to these shortcomings.

The core motivation behind RAG lies in its ability to enhance the quality and relevance of generated text. Here’s how:

• Enriched Context: RAG goes beyond the internal knowledge of an LLM. It retrieves information from external data…