Generative AI – Slide 65 Explained
Understanding the concept shown in Slide 65 with examples, applications, and technical details.
Understanding the concept shown in Slide 65 with examples, applications, and technical details.
Slide 65 focuses on how generative AI transforms raw data into meaningful outputs using trained models. It highlights the flow from input → processing → generation, illustrating the concept of pattern-driven output creation.
Models learn structure from large datasets to predict or generate new data.
User prompts guide the model to shape its output.
Outputs are produced step-by-step based on previous context.
Prompt or raw data is provided.
The model converts input into vector representations.
Model predicts output step-by-step.
Final text, image, or structured result.
Chatbots, summarization, writing assistance.
Artwork, product mockups, design automation.
Simulated data for testing or training models.
It illustrates the transformation from input prompt to generated output through a model pipeline.
Prompts act as constraints guiding the model's generation path.
No, outputs vary because generation uses probabilities.
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