What Software Engineers REALLY Need to Know About Large Language Models | AI APPS #2

Welcome to Nemesis Courses. In this comprehensive introduction to Large Language Models (LLMs), we bridge the gap between traditional software development and AI Engineering.

You don't need a PhD in mathematics to build powerful AI applications. In this lecture, we explain the difference between Machine Learning Engineers and AI Engineers, explore real-world use cases (Amazon, Twitter/X, Support Bots), and dive deep into how LLMs actually predict text.

We also cover the critical concept of "Tokens"—how models count text, calculate costs, and manage context windows—finishing with a practical coding tutorial using Node.js and the tiktoken library.

🚀 What you will learn in this video:

The difference between AI Engineers and ML Engineers.

Real-world architecture: How to integrate LLMs into apps.

What Large Language Models actually are (Probabilistic prediction vs. Intelligence).

The dangers of "Garbage In, Garbage Out" regarding training data.

Deep dive into Tokens, Cost estimation, and Context Windows.

Coding Tutorial: How to count tokens programmatically using JavaScript.

⏱️ Timestamps: 0:00 - Introduction & Course Overview 0:57 - AI Engineer vs. Machine Learning Engineer 1:58 - Real-world AI Use Cases (Amazon, Freshdesk, Redfin) 4:30 - What is a Large Language Model (LLM)? 6:22 - The Importance of Training Data 8:48 - Application Architecture: Frontend, Backend & LLMs 10:58 - Understanding Tokens & Context Windows 13:45 - Token Estimation Coding Tutorial (Node.js)

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