What is Retrieval Augmented Generation (RAG)?

Large Language Models (LLMs) like GPT-4 or Mistral-7b are extraordinary in many ways, yet they come with a set of challenges.

For now, let's focus on one specific limitation: the timeliness of their data. Since these models are trained up to a particular cut-off date, they aren't well-suited for real-time or organization-specific information.

Imagine you're a developer architecting an LLM-enabled app for Amazon. You aim to support shoppers as they comb through Amazon for the latest deals on jackets. Naturally, you want to furnish them with the most current offers available. After all, nobody wants to rely on outdated information, and the same holds true for data queried from your LLM.

This is where Retrieval-Augmented Generation, commonly known as RAG, significantly improves the capabilities of LLMs.

In a way that might resemble a resourceful friend in an exam setting or during a speech who—figuratively speaking, of course—swiftly passes you the most relevant "cue card" out of a ton of information to help you understand what you should be writing or saying next.

Perhaps that wasn't the perfect example, but you get the point. 😄

With RAG, efficient retrieval of the most relevant data for your use case ensures the text generated is both current and substantiated.

RAG, as its name indicates, operates through a three-fold process:

• Retrieval: It sources pertinent information.

• Augmentation: This information is then added to the model's initial input.

• Generation: Finally, the LLM utilizes this augmented input to create an informed output.

Simply put, RAG empowers LLMs to include real-time, reliable data from external databases in their generated text.

For a better explanation, check out this video by Marina Danilevsky, Senior Research Staff Member at IBM Research. She shares two important challenges with LLMs resolved with the help of Retrieval Augmented Generation.

(Credits: IBM Technology)

Knowing about RAG is essential, particularly if considering implementing an Enterprise LLM Architecture for your organization or a personal project. The efficacy of this architecture depends mainly on the strength and utility of RAG.

By now, you should have a basic understanding of RAG and its importance. As we progress through this module, we aim for you to gain a comprehensive grasp of how in-context learning and RAG collectively contribute to making LLMs more effective, current, and enterprise-ready.