Generative AI Tutorial – Slide 84
A clear explanation of the concept shown in Slide 84 with examples, applications, and technical details.
A clear explanation of the concept shown in Slide 84 with examples, applications, and technical details.
Slide 84 introduces how generative models transform input representations into meaningful outputs through a structured reasoning and decoding process. It highlights how models evaluate context, predict the next tokens, and refine outputs.
Input text is broken into tokens, which become numerical vectors for the model to process.
The model weighs relationships among tokens using attention layers to determine meaning.
Probabilities are used to generate coherent text or output results, one step at a time.
Input Encoding
Convert text into embeddings using tokenizers.
Attention Layers
Model learns relationships across tokens.
Probability Prediction
Outputs probability distribution of next possible tokens.
Token Generation
Decodes and outputs next token repeatedly to generate results.
Chatbots, content creation, summarization, translation.
Auto-completion, debugging assistance, code documentation.
Storytelling, design ideas, concept art, music generation.
Workflow optimization, report drafting, knowledge retrieval.
It explains the internal decision-making and generation process of a transformer-based generative model.
They help determine which parts of the input text are most relevant for predicting the next token.
It powers chatbots, assistants, automation tools, and creative generation systems.
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