EP 34 | CME295: LLM Tuning
from AI Bites: The Academic Series Hosted by Jack Lakkapragada
A raw, pre-trained base model isn't very useful—it just wants to endlessly complete sentences. This episode covers the crucial second step: Tuning. We look at how developers take a chaotic text-generator and mold it into a helpful, safe, and conversational assistant. Key Topics: Supervised Fine-Tuning (SFT): Teaching the model how to follow specific instructions and format its answers correctly using high-quality human examples. RLHF (Reinforcement Learning from Human Feedback): How human raters score the model's outputs to guide it toward more helpful and harmless behavior. Alignment: The ongoing challenge of ensuring the model's responses align with human values and safety guidelines. Note: This is an AI-generated study resource created via NotebookLM based on the Stanford CME295 curriculum and personal study notes.
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