How to unlock the full potential of GPT-4 with Retrieval-Augmented Generation (RAG)
Most users of ChatGPT or Gemini’s Family of Models are missing out on the true potential of these LLMs. This is largely due to a lack of awareness about techniques such as building custom GPT wrappers or, more specifically, leveraging Retrieval-Augmented Generation (RAG) to extend their knowledge and utility.
Ok, so…..What is RAG?
Retrieval-Augmented Generation (RAG) is a way to supercharge AI models like GPT-4 by providing them with an extra set of knowledge outside of the training dataset they were trained with.
What does this mean?
Let’s consider an example: building an AI assistant to serve as your Calculus tutor. There are two approaches you can take. The first approach is to ask GPT-4 or GPT-3.5 a calculus-related question and receive a reasonably good response, though with a higher potential for errors. The second, more powerful approach involves extending the LLM to become an expert in Calculus, effectively serving as your dedicated tutor.
How can this be achieved? By providing the LLM with a comprehensive set of knowledge that a Calculus teacher or tutor would possess. Thanks to the capabilities of multi-modal models, which can process various forms of input, you can feed the LLM with documents such as AP Calculus study guides, extensive lists of Calculus problems, or even videos of teachers explaining Calculus concepts. This augmentation significantly enhances the LLM’s expertise in the subject, making it an even more effective and reliable tutor.
Just as data augmentation is used to diversify the dataset when training a machine learning model, Retrieval-Augmented Generation (RAG) aims to enhance existing LLMs. By integrating diverse external data sources in real-time, RAG enriches the model’s responses, making them more accurate and contextually relevant.
Ok, I have told you this, but now what?
So, what’s the next step? The simplest way to get started is by building a custom GPT for a very specific use case directly on OpenAI’s platform. While a subscription is required, it is well worth the investment. Navigate to ChatGPT, click on “Explore GPTs” in the sidebar, and create GPT. I used to tutor AP Computer Science to a couple of students before, so I built this AP CS GPT to assist me in better explaining concepts and solutions to the students.
Or for the more technical folks, you can use LangChain which requires delving into more cool topics like tokenization and embeddings. This approach requires more time and effort, but is well worth it.
By incorporating RAG practices, you can significantly enhance the accuracy, relevancy, and overall effectiveness of your language models. Start exploring RAG techniques today to unlock these benefits.
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