Title: Discussion 3 — 10,000 Examples or 1,000 Curated Ones?
Course: AIINFRA 201: Model Adaptation — Fine-Tuning & Quantization > Section: Week 03 — Dataset Design and Data Preparation for Fine-Tuning

💬 Discussion 3 — 10,000 Examples or 1,000 Curated Ones? (20 pts)

Prompt: Imagine you have exactly one week to prepare a fine-tuning dataset for a real task of your choosing (pick something concrete — a customer-support bot, a code-review assistant, a summarizer for a specific document type, etc.). Would you spend that week collecting 10,000 examples as broadly as possible, or curating a smaller set of roughly 1,000 high-quality examples? Justify your choice using at least one specific idea from this week's lecture (task distribution, coverage vs. correctness, memorization risk, or the LIMA result). Then describe concretely how you would measure whether your choice actually produced a better dataset — what would you check, compare, or test to know you made the right call? Post your initial response, then respond to at least two classmates — ideally engaging with someone who argued for the opposite approach from yours. Push back respectfully, or point out a scenario where their approach might actually be the better call.