#008 - It's RAG Time!

Unlocking the power of Retrieval Augmented Generation in your workflows

Content Vikings

Mar 13, 2024

Transcript

If you've been keeping up with the latest buzz in the world of artificial intelligence, you've probably heard the term "RAG" thrown around, along with a lot of technical jargon that makes it sound like something complicated, that only machine learning experts can understand.

But what exactly is RAG, and why should you care?

In a nutshell, RAG is a technique that combines the power of information retrieval with the magic of AI generation. It's like giving your AI assistant a library card to the most up-to-date information and letting it find exactly what it needs to answer your questions.

LLMs: Powerful Reasoning Engines with a Catch

Large language models (LLMs) are incredible tools – especially as reasoning engines – but they have their limitations when it comes to generating novel outputs. They're only as good as the data they're trained on, which can often be outdated or incomplete. That's where RAG comes in. By integrating real-time, external knowledge into LLM responses, RAG ensures that the information provided is always current and contextually relevant.

Without RAG, using an LLM is like fishing with dynamite. You're going to use way more energy and tokens than necessary to surface a lower quality output. RAG, on the other hand, is like using a precision-guided fishing rod. You get the exact information you need, without all the collateral token waste.

But the way it’s talked about, you’d think it requires a lot of work to set it up. Not so!

RAG Is Everywhere, Even If You Don't See It

Tools like LangChain and LlamaIndex are making it easier than ever for developers to build their own RAG applications. As Manny Silva, head of documentation at Skyflow, puts it:

"If you've interacted with a chatbot that knows about recent events, is aware of user-specific information, or has a deeper understanding of a subject than is normal, you've likely interacted with RAG without realizing it."

But even if you’re not a developer, you can still leverage the power of RAG with existing no-code tools and knowledge management systems.

Notion AI: Bringing RAG to the Masses

Notion, for example, is bringing the power of retrieval augmented generation to tens of millions of people, according to CEO Ivan Zhao by integrating RAG into their new Q&A assistant. With Notion's Q&A, you can ask questions about the contents of your entire Notion workspace, and the assistant will use RAG to find the most relevant information and generate a response.

So, what does this mean for you? Well, if you're a developer, you could spend countless hours trying to build your own RAG system from scratch. But why reinvent the wheel when you can leverage your existing Notion workspace as a knowledge base for RAG and start reaping the benefits right away?

In a recent podcast episode, we conducted a real-time experiment with Notion's Q&A assistant, posing a highly specific question about a previous episode. My "second brain", where I store our podcast transcripts, is extensive. We’re talking tens of thousands of pages. Supplying all this content as input to an LLM – even a large context model like Claude – would be impossible. Instead, the Q&A feature, powered by RAG, uses a semantic vector database. This database links the question's meaning to the entire library's contents, which have been segmented into smaller chunks and indexed for retrieval. Once the relevant chunk or chunks have been found, they’re recombined into the input of the original prompt to provide the answer you’re looking for.

It combines AI’s strengths in both retrieval and generation, but as two distinct steps, with different underlying technology.

The Bottom Line:

But let’s forget about the details and get back to fundamentals.

How does this benefit you, today.

The real value-add of RAG doesn’t come from the technical details. It's in the knowledge bases you’re retrieving from. As more and more out-of-the-box RAG solutions hit the market, the real differentiator will be the quality and specificity of the domain knowledge they're connected to.

This is where you come in. Whether you're a subject matter expert, a hobbyist, or just someone with a unique perspective, you have the power to create your own knowledge bases that can enhance the capabilities of LLMs in ways we've never seen before. By focusing on curating and organizing high-quality, domain-specific information, you can unlock the true potential of RAG and take your AI applications to the next level.

Imagine creating a library of standard operating procedures for everything you do in your job that could conceivably be delegated. Then, hire a virtual assistant and train them on that database. Give them assignments, and when they have questions, refer them to your Second Brain. This is just one example of how you can leverage RAG to enhance your productivity and efficiency.

Not Just Generation, But Transformation

Remember, AI isn't just about generating content. It's about transforming said content in ways that maximize its usefulness. By leveraging RAG and building comprehensive knowledge bases, you can turn your LLMs into true reasoning engines that can provide accurate, relevant, and contextually aware responses to even the most complex queries.

RAG is no longer a complex and esoteric concept reserved for the AI elite. It's becoming more accessible and user-friendly by the day, thanks to tools like Notion AI and the growing ecosystem of RAG-focused platforms and frameworks.

So, what are you waiting for? It's RAG time, baby! It's time to jump on the RAG bandwagon and start building your knowledge base.

Did this article give you an idea for how you will use AI differently? If so, drop us a comment or a question.

Show Notes:

In this episode, we dive into the world of Retrieval Augmented Generation (RAG) and explore how it's revolutionizing the way we interact with AI. RAG combines the power of information retrieval with AI generation to create more accurate, up-to-date, and contextually relevant responses from large language models (LLMs).

We discuss how RAG is becoming increasingly accessible and user-friendly, thanks to tools like Notion AI and the growing ecosystem of RAG-focused platforms and frameworks. By integrating RAG into their new Q&A assistant, Notion is bringing the power of this technology to tens of millions of people, allowing users to ask questions about the contents of their entire workspace and receive highly relevant, generated responses.

We also conduct a real-time experiment with Notion's Q&A assistant, demonstrating how RAG uses a semantic vector database to link a question's meaning to the contents of an extensive knowledge base, segmented into smaller, indexed chunks. This enables the assistant to retrieve relevant information and generate accurate answers, even for highly specific queries.

Summary/TLDR:

RAG combines information retrieval with AI generation for more accurate, up-to-date, and contextually relevant LLM responses
RAG is becoming more accessible and user-friendly through tools like Notion AI
Real-time experiment with Notion's Q&A assistant demonstrates the power of RAG
The quality and specificity of domain knowledge will be the key differentiator for RAG solutions
Practical applications include creating knowledge bases for job tasks and training virtual assistants
Call to action: start building knowledge bases and leverage RAG to transform AI applications

Keywords: Retrieval Augmented Generation, RAG, large language models, LLMs, Notion AI, Q&A assistant, knowledge bases, domain-specific information, AI applications, reasoning engines, semantic vector database, information retrieval, AI generation, virtual assistants, productivity, efficiency.