Model Distillation: Smaller Models Trained From Larger

Model distillation enables smaller models to approximate larger ones; useful for cost and latency.

What AI does well here

• Distill when latency or cost is critical and quality acceptable
• Test distilled model quality against original on your use case
• Maintain access to original for fallback or quality-sensitive cases
• Plan for re-distillation as base models improve

What AI cannot do

• Get full base model capability from distilled model
• Substitute distillation for use case clarity
• Eliminate the quality trade-off

Practice this safely

Use a small project example from your own work. The useful move is to compare the AI's draft against your goal, sources, and constraints before you trust it.

1. 1Ask AI to explain model size in plain language, then underline anything that sounds uncertain or too broad.
2. 2Give it one detail from "Model Distillation: Smaller Models Trained From Larger" and ask for two possible next steps plus one reason each step might be wrong.
3. 3Check cost optimization against a trusted source, teacher, adult, expert, or original document before you use it.