RAG: What’s the Need?

A month ago, I attended a Google Developer Group Workshop on GenAI and came across RAG. While this topic might be considered old, it certainly piqued my curiosity.

Folks, we cannot emphasize enough how important RAG is. Let’s delve into why.

Current LLMs face two main challenges:

1. LLMs providing Out of date answers
2. No source of Data

Have you encountered this issue? I’m sure most of you have! This occurs because LLMs are trained to retrieve specific data from the dataset they were trained on, dated to a particular time.

The above answer by ChatGPT illustrates this limitation — it doesn’t know the answer as it was trained only up until September 2023. In the worst-case scenario, it may provide an answer, but it could be obsolete, leading to potential misinformation.

The next challenge LLMs face is the lack of source attribution. You can’t blindly trust the data provided by ChatGPT without knowing its source, can you?

So, how does RAG address these issues?

According to IBM, Retrieval Augmented Generation (RAG) is an AI framework designed to retrieve facts from an external knowledge base. This allows large language models (LLMs) to ground their responses in the most accurate and up-to-date information, providing users with insights into the generative process of LLMs.

Let’s simplify this with an example:

Imagine asking ChatGPT to retrieve the latest football match scores. By connecting ChatGPT to a real-time database containing all the football scores, we can fetch updated information within seconds.

Benefits:

• Avoids the need for retraining language models by augmenting them with up-to-date information.
• Provides source attribution, allowing users to know where the data originated.
• Enables real-time information access and integration: RAG can connect LLMs directly to live social media feeds, news sites, or other frequently updated sources.
• Enhances accuracy.

Considerations:

• The augmented data must be of good quality and relevance.
• Ensure the model uses the augmented data accurately and responds appropriately to the prompt.

In essence, RAG helps you find up-to-date data with a clear source. With RAG, you can trust that the information is relevant and reliable. I hope you found this quick introduction to RAG helpful.

Future of RAG:

Based on research findings and startup practices, RAG is the most effective solution currently available and is unlikely to be replaced in the next five years. However, in the long run, as smarter architectures evolve, they may eventually surpass RAG’s capabilities.

Cheers to the bright future of RAG! It’s an exciting time for AI advancements, and RAG seems to be leading the way toward more accurate, reliable, and up-to-date information retrieval.