Lesson 2006 of 2116
Fine-Tune vs Prompt: When AI Tuning Pays Off
Fine-tuning is rarely the right answer for most teams — here's when it actually is.
Lesson map
What this lesson covers
Learning path
The main moves in order
- 1The premise
- 2fine-tune
- 3prompt-engineering
- 4tradeoff
Concept cluster
Terms to connect while reading
Section 1
The premise
99% of use cases are solved with prompts and RAG. Fine-tuning is for narrow, repeated tasks where format matters more than content.
What AI does well here
- Lock in a specific output format reliably.
- Reduce token usage on repeated structured tasks.
- Bake in tone or style for one specific app.
- Slightly improve speed and cost for high-volume tasks.
What AI cannot do
- Add new factual knowledge effectively (use RAG instead).
- Justify the cost for low-volume use cases.
Key terms in this lesson
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