Most developers have experimented with ChatGPT or GitHub Copilot. But when it comes to building AI-powered applications, simply calling an LLM API isn't enough. Understanding what's happening behind the scenes helps you design systems that are scalable, reliable, and cost-effective.
In this article, we'll explore four concepts every software engineer should know: tokens, embeddings, transformers, and Retrieval-Augmented Generation (RAG).
1. LLMs Think in Tokens, Not Words
One of the biggest misconceptions about Large Language Models (LLMs) is that they understand words like humans do. In reality, they process tokens, which are smaller units of text.
For example:
