Generative AI 101

Every design decision about a GenAI product starts with what kind of model it needs and what the model actually does under the hood.

Generative AI 101 explains the two architectures behind almost every generative product. Autoregressive models such as GPT, Claude, Gemini, and Llama produce text one token at a time. A token is a small unit of text, and every additional token requires another pass through the model, which is why latency and cost grow with response length. Diffusion models such as Midjourney, Stable Diffusion, and Sora work differently: they start from random noise and refine it step by step into a coherent image or video. Multimodal systems connect the two by mapping text and images into a shared vector space.

The module then widens the frame. The model is only one component of a working system. Training data determines capability, so cleaning, deduplication, filtering of harmful content, and removal of personal information all happen before training starts. Parameters are the billions of internal weights that store learned patterns. The context window caps how much text a model can process in one request. Time to first token and tokens per second are the two inference metrics that drive most product latency decisions.

After reading, you will be able to ask which model family fits a task before anyone discusses prompts. You will know the three license categories, proprietary, open-weight, and open-source, and why most production systems route traffic across more than one model. You will also understand why Mixture of Experts designs deliver more capability per unit of compute at the cost of higher memory.

The failure modes are predictable. Teams pick one frontier model for every workload and spend their budget on trivial queries. They ignore context window limits until users see truncated answers. They treat scores on benchmarks such as MMLU and HumanEval as proof of production readiness, even though benchmark contamination means final selection must rest on your own evaluation data. Treat the model as replaceable; the data layer and the evaluation harness should outlast any single vendor.

In one paragraph

Generative AI products are built on one of two foundational architectures. Autoregressive models such as GPT, Claude, Gemini, and Llama generate text one token at a time, which is why latency and cost scale with response length. Diffusion models such as Midjourney, Stable Diffusion, and Sora start from random noise and iteratively denoise it into a coherent image or video. Modern systems combine these with multimodal capabilities by mapping text and images into a shared vector space. The model is only one piece; data pipelines, training methods, evaluation, safety, and serving infrastructure complete the system.

This module is part of the Foundations track in the free AI Learning Hub. Source material: Generative AI 101.