Generative AI for Social Media Content Categorization - From Theory to Production

Overview

Discover how to combine RAG & LLMs for scalable YouTube channel categorization, with practical examples, production insights, and live demonstrations of AI-powered content analysis.

Syllabus

• Course Introduction
Overview of Generative AI in Content Categorization
Course Objectives and Outcomes
Introduction to YouTube Channel Use-Case
• Fundamentals of Generative AI
Basics of Generative Models
Introduction to Retrieval-Augmented Generation (RAG)
Understanding Large Language Models (LLMs)
• Deep Dive into RAG and LLMs
Mechanisms of Retrieval-Augmented Generation
Training and Fine-tuning LLMs for Content Categorization
Comparing RAG to Traditional Methods
• Building a Scalable Categorization System
Designing a Workflow for YouTube Categorization
Tools and Technologies Required
Data Collection and Preprocessing Techniques
• Practical Examples
Implementing RAG for Sample YouTube Channels
Hands-on Exercises: Training Models with Example Datasets
Evaluating Results and Fine-Tuning Models
• Production Insights
Scaling Solutions for Real-World Applications
Automation and Real-Time Categorization
Error Handling and Model Maintenance
• Live Demonstrations
Setting Up a Live Demo Environment
Real-Time Content Analysis and Categorization
Interactive Q&A and Troubleshooting
• Case Studies
Analysis of Successful AI-Powered Content Categorization Systems
Lessons Learned and Best Practices
• Conclusion and Future Trends
Recap of Key Learnings
Emerging Trends in Generative AI and Content Analytics
Resources for Ongoing Learning and Development
• Final Project
Design and Create a RAG-based Categorization System
Present Findings and Insights from the Project
• Additional Resources
Recommended Reading and Tools
Online Communities and Forums for Continued Support

Subjects

Data Science