Generative AI Tutorial – Slide 95
A deep explanation of the concept illustrated in Slide 95, including examples, applications, and the underlying technical mechanisms.
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A deep explanation of the concept illustrated in Slide 95, including examples, applications, and the underlying technical mechanisms.
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Slide 95 focuses on the concept of **Generative Model Evaluation**, particularly how generated outputs are judged for coherence, correctness, quality, and alignment with user intent.
It highlights the gap between human expectations and model behavior, illustrating how evaluation strategies help train and refine generative systems.
Measures if AI responses are relevant, factual, safe, and useful.
Trained from human feedback to guide the model toward desirable behavior.
Ensures outputs reflect user intention and ethical boundaries.
Model creates output from a prompt.
Humans or automated tools review the output.
Outputs receive reward scores.
Model parameters are updated for improvement.
Human feedback is used to improve tone, clarity, and relevance.
Ensures AI-generated articles or explanations meet accuracy and style requirements.
Helps the model avoid harmful or biased outputs.
Feedback helps models tailor content to user preferences.
It guides the model toward safer and more useful outputs.
Yes, especially for safety and subjective quality metrics.
Reward scores are used to tune model behavior via reinforcement learning or fine-tuning.
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