What Is Retrieval-Augmented Generation and How to Make AI Work for You, with Guil Hernandez

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Dec 20, 2023

Guil Hernandez, developer and educator with 15 years of experience, explores the concept of retrieval-augmented generation in AI engineering. He explains how AI models turn inputs into embeddings and teaches how to connect models to specific data sources for better results. The episode covers the applications of retrieval-augmented generation, including building a chatbot that provides career advice and transcribing calls. Differences between retrieval-augmented generation and fine tuning are also discussed.

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Introduction

00:00 • 2min

Retrieval Augmented Generation (RAG) and its Role in AI Engineering

01:54 • 13min

Exploring Retrieval-Augmented Generation for AI Models

15:14 • 2min

Retrieval-Augmented Generation (RAG) vs Fine Tuning

17:36 • 6min

Concluding the Episode and Encouraging Listener Engagement

23:45 • 3min

🎙 About the episode

Meet Guil Hernandez 🇺🇸! He is a developer and educator with over 15 years of experience in tech. He's also a Scrimba teacher who is a part of the team bringing you the AI Engineer Path, and in this episode, he's helping us understand retrieval-augmented generation.

In the previous episode, Tom Chant helped us understand the world of AI models. Today, Guil will further teach us how these models work under the hood. AI models don't understand the world like we do. When we interact with them, they turn our inputs into mathematical representations known as embeddings. By creating our own embeddings, we can teach AI to do what we want it to.

Today, we're getting an introduction about making a model aware of your own data source so that that data can be considered for the AI output. For example, using the techniques you'll learn from Guil in this episode, you could connect a model to your customer support conversations so that the model knows what is necessary to answer unique questions about your (or your client's) business.

This is the third episode of our series on AI engineering, introducing Scrimba's AI Engineer Path. This path is your gateway to unlocking the full potential of AI for your projects.

🔗 Connect with Guil

⏰ Timestamps

Guil focuses on RAG and embeddings (01:42)
RAG makes a foundation model aware of your data (03:14)
Spotify has been using RAG since 2014 (05:56)
How embedding works: embedding model + vector database + generative model (09:00)
You're enhancing content retrieved from a database with a generative model (10:26)
A foundation model can't just understand text (10:34)
What's a vector database? (12:35)
Can we make an AI chatbot for the Scrimba podcast? (15:05)
You can chunk the files directly at OpenAI now! (16:49)
OpenAI's Assistants API (17:33)
AI is evolving quickly (19:07)
Assistants API does RAG (19:55)
What is fine-tuning? (20:39)
Differences between RAG and fine-tuning (21:14)
Community break with Jan the Producer (23:58)

🧰 Resources Mentioned

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