Lesson 1425 of 2116
Fine-Tuning Cost Curves: When Fine-Tuning Pays Off
Compute the break-even point for fine-tuning vs. continued prompting across model families.
Lesson map
What this lesson covers
Learning path
The main moves in order
- 1The premise
- 2fine-tuning ROI
- 3break-even
- 4prompt cost
Concept cluster
Terms to connect while reading
Section 1
The premise
Fine-tuning pays off only at sustained volume with stable tasks — math, not feels, should drive the choice.
What AI does well here
- Compute prompt-cost vs. training+inference-cost over time.
- Estimate quality improvement on representative eval set.
- Plan for re-training as base models update.
What AI cannot do
- Guarantee quality improvement without an eval baseline.
- Avoid retraining when base models change.
Key terms in this lesson
End-of-lesson quiz
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Tutor
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