Generative AI Tutorial – Slide 47
Explanation, technical insight, and real-world applications
Explanation, technical insight, and real-world applications
Slide 47 illustrates the step-by-step structure of how generative AI models transform input data into meaningful outputs. It highlights the idea of information flow through embeddings, model inference, and output generation.
Data enters the model converted into embeddings—dense numerical vectors capturing meaning.
Transformers process context using attention, enabling the model to evaluate relationships between tokens.
The model produces predictions one step at a time, forming coherent text, images, or other media.
Prompt or data is received.
The model converts input into vectors.
Attention mechanisms process context.
Model predicts next tokens to produce final content.
Writing assistance, stories, summaries, chatbots.
Art creation, product design, concept visuals.
Automated coding, debugging, and API generation.
Classification, extraction, semantic search.
It visualizes the internal flow of data through a generative model pipeline.
They convert raw input into a dense mathematical form the model can process.
By predicting token sequences one step at a time using learned probability distributions.
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