Lesson 1966 of 2116
When Fine-Tuning Beats Prompting (and When It Doesn't)
Fine-tune for style and format consistency, not for new knowledge.
Lesson map
What this lesson covers
Learning path
The main moves in order
- 1The premise
- 2fine-tuning
- 3prompting
- 4trade-off
Concept cluster
Terms to connect while reading
Section 1
The premise
Fine-tuning shines for narrow style/format tasks at scale. For new facts or fast-changing knowledge, retrieval beats fine-tuning.
What AI does well here
- Learn a consistent output style from many examples.
- Reduce token cost on a high-volume narrow task.
What AI cannot do
- Reliably absorb new factual knowledge from examples.
- Update what it knows without another training run.
Key terms in this lesson
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