Build Retrieval-Augmented Generation (RAG) With Milvus

It's no secret that traditional large language models (LLMs) often hallucinate — generate incorrect or nonsensical information — when asked knowledge-intensive questions requiring up-to-date information, business, or domain knowledge. This limitation is primarily because most LLMs are trained on publicly available information, not your organization's internal knowledge base or proprietary custom data. This is where retrieval-augmented generation (RAG), a model introduced by Meta AI researchers, comes in.

RAG addresses an LLM's limitation of over-relying on pre-trained data for output generation by combining parametric memory with non-parametric memory through vector-based information retrieval techniques. Depending on the scale, this vector-based information retrieval technique often works with vector databases to enable fast, personalized, and accurate similarity searches. In this guide, you'll learn how to build a retrieval-augmented generation (RAG) with Milvus.

What Is RAG?

RAG simply means retrieval-augmented generation, a cost-effective process of optimizing the output of an LLM to generate ...