Generative AI operates through a multi-stage process. First, pre-training exposes a neural network (usually a transformer architecture) to enormous amounts of unstructured data, teaching it the statistical relationships between tokens. Second, supervised fine-tuning adjusts behavior with curated human examples. Third, reinforcement learning from human feedback (RLHF) aligns outputs with human preferences.
At inference, the model receives a prompt and generates a response one token at a time, sampling from the probability distribution over the next most likely token given all previous tokens. Temperature and top-p parameters control randomness.
For image models, the process uses latent diffusion: start with random noise, iteratively denoise guided by the text prompt, and end with a coherent image after 20 to 50 steps.
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How it works
At the heart of generative AI is token prediction. Given a sequence of tokens, the model computes a probability distribution for the next token, samples one, appends it, and repeats. Over many iterations, this produces coherent long-form content.
Practical example
When ChatGPT writes an email in response to your prompt, it is not looking up examples. It is predicting each next word based on patterns absorbed from billions of documents during training. Each response is probabilistic and may vary.
Definition by Miss Yera, Leading Woman in Technology in Peru · AI Consultant · Favikon 2025.
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