intermediate~3h
Prompting vs. RAG vs. Fine-tuning: A Decision Framework
A 2-axis decision matrix (external knowledge needed vs. behavior adaptation needed) for choosing between prompt engineering, RAG, fine-tuning, or a hybrid — plus a deeper 3-way comparison of full fine-tuning, LoRA, and RAG.
Prompting vs. RAG vs. Fine-tuning — Decision Matrix
Plotted against two axes: how much external knowledge you need, and how much behavior/style adaptation you need.
| External knowledge needed | Behavior/style adaptation needed | Recommended approach | |
|---|---|---|---|
| Low / Low | Low | Low | Prompt engineering suffices |
| High / Low | High | Low | RAG |
| Low / High | Low | High | Fine-tuning |
| High / High | High | High | Hybrid (RAG + Fine-tuning) |
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Subtopics
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Exercises
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Projects
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Quiz Qs
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Flashcards
▶📚 Prerequisites(2)
🎓 Learning objectives
- •Apply the 2-axis decision framework (external knowledge vs. behavior adaptation) to pick the right approach for a new LLM application
- •Explain why RAG changes what a model knows while fine-tuning changes how it behaves
- •Compare full fine-tuning, LoRA, and RAG along the 'what gets modified' and 'what gets stored' dimensions
- •Identify RAG's specific structural limitations (question/answer mismatch, unsuitability for summarization)
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