Generative AI – Slide 14
Explanation, applications, and technical insight into the concept shown on the slide.
Explanation, applications, and technical insight into the concept shown on the slide.
Slide 14 introduces how generative AI models transform input prompts into structured, meaningful outputs. It highlights the shift from rule‑based systems to neural models that learn patterns directly from data, enabling them to generate text, images, audio, or code.
Models learn compact internal representations from massive datasets, enabling them to understand context and structure.
Transformers predict the next item in a sequence, forming the basis of language, image token, and audio generation.
Outputs depend on instructions, examples, or constraints provided by the user.
User provides a prompt or sample.
Model converts the input into embeddings.
Neural network predicts tokens step by step.
System returns text, image, audio, or code.
It predicts the most likely next token based on learned patterns.
Large collections of text, images, audio, or code depending on the model type.
No. Models use probability sampling, making outputs vary with temperature settings.
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