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AI Engineering with Python
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Module 1 · LLM API Fundamentals · Recap
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RAG: why retrieval beats raw prompting
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📝 **Question:** When should you choose RAG over fine-tuning a model? 📋 Pick the right answer. 💡 **Hint:** Re-read the theory above if unsure.
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📝 **Question:** When should you choose RAG over fine-tuning a model? 📋 Pick the right answer. 💡 **Hint:** Re-read the theory above if unsure.
A
Always — modern open-weights and per-tenant LoRA adapters have made fine-tuning slower and more expensive than retrieval for every workload
B
When your knowledge base changes frequently, you need to show citations, and your budget can't sustain regular fine-tuning runs
C
Only when you're locked to a smaller model and need to compensate for its weak reasoning by stuffing every relevant doc into context
D
Never — fine-tuning embeds knowledge directly into weights and so beats RAG on latency, accuracy and operability for production work
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