Module 4: LLMs & Generative AI - How Language Models Actually Work

Why did large language models go from research curiosity to executive agenda in eighteen months?

Large language models are not magic and they are not glorified autocomplete. They sit somewhere specific on the technology curve, and the people building serious products on top of them understand exactly where that is. This module explains how an LLM actually works, what it can and cannot do, and how to think about the trade-offs that matter when you put one into production.

What you'll learn in this module

• How tokens, embeddings, and the attention mechanism turn text into useful predictions
• The difference between pretraining, fine-tuning, RLHF, and in-context learning, and what each costs in time and money
• Why hallucination is a structural property of LLMs and the techniques (retrieval, tool use, constrained decoding) that mitigate it
• The frontier-model versus open-weight-model trade-off in 2026, including cost, latency, IP, and data-residency consequences
• How prompt engineering, RAG, function calling, and agent frameworks fit together in real deployments

The full module connects the underlying mechanics of LLMs to the architectural choices that determine whether a generative AI product ships and scales or stalls in pilot.