How does generative AI work?

Generative AI works by training neural networks on large datasets to learn statistical patterns in the data, then using those learned patterns to produce new outputs that follow similar distributions — with transformer architectures enabling modern text generation and diffusion processes enabling image generation.

Training: learning from examples

Generative AI models are trained by exposing them to large datasets and optimizing them to predict or reconstruct their training data. For language models, training typically involves next-token prediction: given a sequence of text, learn to predict the next token. Doing this across billions of examples causes the model to learn the statistical structure of language — grammar, factual associations, reasoning patterns, and stylistic conventions — as a byproduct of becoming good at prediction. For image generation models using diffusion, training involves learning to reverse a process that progressively adds noise to images, so the trained model can start from noise and iteratively refine it into a realistic image. The model does not explicitly learn rules; it learns a compressed representation of patterns in data.

Inference: generating from learned patterns

At inference time, a generative model takes a prompt or conditioning input and uses its learned patterns to produce output. For a language model, the process is autoregressive: the model generates one token at a time, with each token conditioned on the prompt and all previously generated tokens. Sampling temperature controls the randomness of token selection — lower temperature produces more predictable, focused output; higher temperature produces more varied, sometimes creative output. For image generation models, inference typically involves iterative denoising steps, starting from random noise and applying the learned denoising function repeatedly until an image emerges that matches the conditioning prompt. The number of steps, the guidance strength, and other parameters affect the quality and character of the output.

Alignment: shaping behavior through additional training

Base models trained purely on next-token prediction produce outputs that follow the statistical patterns of their training data, which may not be safe, helpful, or appropriately formatted for use as an assistant. Alignment techniques — particularly reinforcement learning from human feedback (RLHF) and related approaches — add a second training stage that teaches the model to produce outputs that humans rate as preferable: helpful, honest, and avoiding harmful content. This alignment training is what turns a base language model into a usable assistant that follows instructions, refuses harmful requests, and produces structured responses rather than raw statistical continuations. The alignment process introduces its own limitations and failure modes, but it is what makes deployed language models substantially different from the raw models that emerge from pretraining.

How does generative AI work? — FAQ

Why do generative AI models hallucinate?

Hallucination occurs because generative models produce outputs based on learned statistical patterns rather than verified facts. The model does not have a separate knowledge base it looks up answers from — it generates text that follows the patterns of text in its training data, which includes both accurate and inaccurate content. When a question has a well-defined answer that appears consistently in training data, the model usually gets it right. When a question requires specific facts that are sparsely represented or absent from training data, the model generates a plausible-sounding completion that may be factually wrong.

Is generative AI deterministic or random?

Generative AI is probabilistic: given the same prompt, it can produce different outputs on different runs because the output is sampled from a probability distribution over possible next tokens rather than deterministically computed. Setting temperature to zero makes the output deterministic — always selecting the highest-probability token — but even at zero temperature, small differences in implementation or numerical precision can produce variation. In practice, most production applications use temperatures above zero to avoid outputs that are stilted or repetitive, accepting some run-to-run variation as a trade-off for output quality.