How LLMs Actually Work: Tokens, Embeddings, and Transformers Explained

<p>By the end of this project, you'll be able to explain exactly what happens inside an LLM API call: how text is broken into tokens, how those tokens become meaning-bearing embeddings, how transformer self-attention builds context-aware understanding, how models move from expensive one-time training to cheap repeated inference, and how to tune the context window and temperature for reliable, production-grade behavior. Three hands-on labs put tokenization, embeddings, and context-window budgeting directly into your own hands with real code.</p>

Tools: python, tokenization, embeddings, transformers