What Is Retrieval-Augmented Generation (RAG)?
Explore how retrieval-augmented generation (RAG) combines retrieval systems with generative models to produce accurate and context-aware outputs. Understand RAG's architecture, key processes like indexing and retrieval, its advantages over traditional approaches, challenges faced, and common use cases such as customer support and question answering.
Imagine a seasoned computer scientist well-versed in countless research papers, programming languages, and complex problems across critical domains like algorithms and machine learning. Despite their vast knowledge, they might not be fully updated on every new technological development, exhibiting gaps shaped by their unique experiences and the era of their initial training.
Similarly, foundation models, such as large language models, mirror this scenario. Trained on extensive but static datasets, these models often reflect the data's incompleteness, recency, and biases. While they can generate plausible information, they are prone to producing outdated or incomplete responses and may even generate plausible yet incorrect details, a phenomenon known as hallucinations. Traditional methods like
This is where retrieval-augmented generation comes into play. ...