Generative AI – Slide 63
Understanding the concept illustrated on this slide with examples, applications, and technical explanation.
Understanding the concept illustrated on this slide with examples, applications, and technical explanation.
Slide 63 explains how Generative AI models convert an input representation into a refined output by using learned statistical patterns. The slide emphasizes structured generation, model understanding, and output alignment.
Models convert raw inputs into embeddings capturing meaning and context.
Neural networks transform embeddings through layers to generate outputs.
Outputs are produced token-by-token or step-by-step using learned probabilities.
Prompt, text, or image provided to the model.
Model encodes input into high‑dimensional vectors.
Transformer layers compute next-step predictions.
Final generated text, image, or action.
It illustrates the flow from input to generated output using model layers and probabilistic selection.
They allow the model to understand meaning in a numerical form.
The structure is similar, but image models use pixel or patch embeddings.
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