Have you ever wondered how AI can become even smarter by combining its own capabilities with a search engine like Google?
If so, you're in the right place!
Today, we'll dive into Retrieval Augmented Generation, or RAG, and uncover how it takes AI's performance to the next level.
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Here's what you'll get from watching this video:
Understand what Retrieval Augmented Generation (RAG) is
Learn how RAG enhances AI's response accuracy
Find out practical applications of RAG in real-world scenarios
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So, what exactly is RAG?
Think of it as connecting a powerful search engine to your AI.
When you type in a prompt, the service first does a search—this could be on documents you've provided, the web, or any other source of information.
It then extracts relevant information and adds it to your prompt to provide more context.
The AI then uses this enriched prompt to generate a much more informed and accurate response.
Simple in concept, right?
Let's go through a practical example.
Imagine you have a favorite book, and you upload it to the AI.
The AI breaks the book down into individual pages and stores them like files in a digital cabinet.
When you ask the AI a question about the book, say about a particular character, it'll search through those "files", find the pages that mention the character and any additional context, and then incorporate this specialized knowledge into its response.
Why would you want to do that?
Well, it offers several benefits.
First, it makes the AI much more accurate.
For example, if you ask, "Tell me about John" there could be millions of Johns.
But if you add in pages from your book that talk about John, the AI will know exactly which John you mean.
Secondly, it can allow the AI to know information it wouldn’t otherwise have, like details from a book you're currently writing.
Lastly, RAG can also save resources by only sending the most relevant information when querying an LLM, making the prompts shorter and less expensive.
To recap, today we explored how RAG can turn your AI into an even more powerful, accurate, and resource-saving tool by enriching it with context-specific information.
Thanks for watching!
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