Have you ever wanted to create your AI app but felt daunted by coding and technical hurdles? Your wait is over. Now, with OpenAI’s accessible platform, crafting a custom AI to enhance your workflows and assist others in your field is not just a dream — it’s a reality.

Last week, Kenneth Lo provided a great webinar on how to create your own custom GPT. If you missed it, do not worry. Check out the recorded session below:

This Weeks Events

This week, we, Hamza Farooq, will discuss AI complex search functionality using RAG. If you’ve been following the recent AI hype, you must have heard “RAG” quite often. RAG stands for Retrieval-Augmented Generation, a critical step in enhancing the power of AI chatbots. However, this term isn’t as popular as phrases like ‘prompt engineering,’ probably because abbreviations are generally less intuitive to understand and remember :)

What is RAG? At its core, RAG is a method used in AI to enhance how machines understand and generate information. While Large Language Models are good at summarizing and forming sentences like natural language, RAG gives its extended capabilities to provide additional information.

Let’s say you are writing a letter, and there’s a magical mailbox that can write back to you. This mailbox contains all the letters people have written worldwide (i.e., it’s a large language model), so it can generate responses based on what they have learned from those letters, almost like magic. This is how traditional LLMs or AI chatbots utilize their “existing knowledge.”

But sometimes, you might want to ask about something more specific, like a recipe for a cake, a math problem, or “What’s the weather tomorrow?” These queries require particular knowledge or data sources that people might not have written about in the mailbox — and this is where RAG comes in.

• Date: Wednesday, March 13, 2024

• Presenter: Hamza Farooq, adjunct Professor at UCLA and Stanford

• Register: HERE