Generative AI – Slide 60 Explained
A clear explanation of the concept shown in Slide 60, including examples, technical details, and real-world applications.
A clear explanation of the concept shown in Slide 60, including examples, technical details, and real-world applications.
Slide 60 introduces the idea of *Generative Model Fine-Tuning and Alignment*, focusing on how models learn specialized behaviors using training examples, feedback loops, and optimization strategies. This stage is crucial for shaping models into domain‑specific assistants capable of following user intent safely and accurately.
Training the model on curated input–output pairs to guide behavior.
Using human or automated scoring to refine responses and reduce harmful outputs.
Models trained to rank outputs; they steer the generative model during optimization.
Gather high‑quality prompts and preferred outputs.
Fine‑tune the base model on curated examples.
Train a scorer based on human preferences.
Use reinforcement learning to refine model outputs.
Medical, legal, financial, and academic models built using domain‑specific fine‑tuning.
Blogs, code, layouts, game assets, and storytelling enhanced via reward‑aligned tuning.
Models refined to avoid harmful, biased, or inaccurate outputs.
Fine‑tuned agents capable of repetitive tasks with controlled accuracy.
Base models are generalists; fine‑tuning makes them specialized and safer for targeted use.
Curated examples, domain‑specific datasets, human‑rated responses, and preference comparisons.
It improves alignment but requires careful tuning to avoid unintended behavior.
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