Generative AI Tutorial – Slide 16
Understanding the concept shown in Slide 16 with examples, applications, and a technical walkthrough.
Understanding the concept shown in Slide 16 with examples, applications, and a technical walkthrough.
Slide 16 introduces how generative models transform inputs into new content by recognizing patterns, embeddings, and context relationships. It highlights how models generate meaningful output based on learned structures rather than memorization.
Numerical vector representations capturing meaning and relationships between inputs.
The model’s working memory that determines how much information it can consider at once.
The model generates output one token at a time based on probability distributions.
User enters text, prompt, or data.
Model converts text into embeddings.
AI predicts next tokens and creates new output.
Final content returned to user.
Articles, scripts, summaries.
Code completion, debugging, automation.
Images, audio, storyboarding.
It ensures each step considers context and probabilities.
No, it uses patterns learned from training to generalize.
Prompt design, model size, context window, and data quality.
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