Dip Your Toes into AI
5 Beginner-Friendly Projects to Learn LLMs & RAG
The world of Large Language Models (LLMs) and Retrieval Augmented Generation (RAG) can seem daunting, but it doesn't have to be! If you're eager to learn about these powerful AI technologies, there's no better way than diving in and building something yourself.
This post outlines five beginner-friendly projects that gradually introduce you to the core concepts of LLMs and RAG. No prior experience is required —no problem! We'll start with the basics and work our way up.
1. Your First LLM API Call: A Simple Q&A Chatbot
You can start your journey by interacting with a pre-trained LLM like GPT-4 through its API. This project involves sending a question to the API and displaying the response. It's a simple yet effective way to understand how LLMs process information and generate human-like text.
2. Summarizing Videos with LLMs
Take a step further and use an LLM to summarize YouTube videos. You'll learn to extract the audio from a video, transcribe it into text using a speech-to-text API, and then feed it to an LLM for summarization. This project combines multiple AI technologies and showcases the practical applications of LLMs.
3. Building a RAG Pipeline from Scratch
Ready to build your own RAG system? This project guides you through creating a basic RAG pipeline. You'll learn how to:
Create a vector database to store and search your data.
Use embeddings to represent text as vectors.
Retrieve relevant information from the database based on a user's query.
Generate comprehensive answers by combining retrieved information with LLM capabilities.
4. Fine-tuning LLMs on a Custom Dataset
Fine-tuning allows you to adapt a pre-trained LLM to your specific needs. In this project, you'll create a custom dataset and use it to fine-tune a model. This will give you a deeper understanding of how LLMs learn and how to customize them for specific tasks.
5. Building a Context-Aware Chatbot with RAG
You can combine your knowledge of LLMs and RAG to build a context-aware chatbot. This project involves creating a chatbot that can access and retrieve information from a knowledge base to answer questions accurately and comprehensively.
Why These Projects?
These projects are designed to be:
Progressive: They gradually increase in complexity, allowing you to learn at your own pace.
Practical: They demonstrate real-world applications of LLMs and RAG.
Engaging: They challenge you to build something unique and interesting.
By completing these projects, you'll gain a solid foundation in LLMs and RAG, setting yourself up for more advanced AI endeavors. So, what are you waiting for? Start building!