Lesson 2064 of 2116
Fine-Tuning vs Prompting vs RAG: Choosing the Right Tool
When to fine-tune, when to prompt-engineer, and when to retrieve.
Lesson map
What this lesson covers
Learning path
The main moves in order
- 1The premise
- 2fine-tuning
- 3prompt engineering
- 4RAG
Concept cluster
Terms to connect while reading
Section 1
The premise
Most AI projects reach for fine-tuning too soon. The default order should be: better prompts → RAG → fine-tuning, with each step justified by clear evidence the previous tier is insufficient.
What AI does well here
- Improving accuracy 80% of the time just with better prompting
- Adding domain knowledge through RAG instead of training
- Fine-tuning when you need a specific output format or style consistently
- Distilling a large model's behavior into a smaller, cheaper one
What AI cannot do
- Fine-tune your way out of fundamentally bad data
- Fine-tune private knowledge in safely — RAG is usually safer
- Make fine-tuned models update easily as data changes
Key terms in this lesson
End-of-lesson quiz
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