Lesson 1599 of 2116
Distillation Tradeoffs: When Smaller Models Quietly Lose
Distilled models look great on aggregate evals but quietly lose long-tail capabilities — the tradeoff matrix matters for production decisions.
Lesson map
What this lesson covers
Learning path
The main moves in order
- 1The premise
- 2distillation
- 3long-tail capability
- 4eval coverage
Concept cluster
Terms to connect while reading
Section 1
The premise
AI can frame distillation tradeoffs and design eval coverage, but actual production decisions need workload-specific testing.
What AI does well here
- Draft tradeoff matrices comparing teacher vs distilled across capability dimensions.
- Generate long-tail eval prompts to surface hidden regressions.
What AI cannot do
- Decide acceptable capability loss for your workload.
- Replace production shadow-traffic testing.
Key terms in this lesson
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