When to choose between RAG, Prompting and Fine-tuning?
AI Product Management Interview Question: In what situations would you explicitly avoid using RAG and choose prompting or fine-tuning instead?
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AI Product Management Interview Question -
In what situations would you explicitly avoid using RAG and instead choose prompting or fine-tuning?
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We can choose between retrieval-augmented generation (RAG), prompting, and fine-tuning by evaluating four practical dimensions:
Knowledge volatility - How often does the information change? If it changes frequently, external retrieval becomes more attractive.
Latency and reliability - How fast and predictable must responses be? Retrieval adds network and compute hops, and more failure modes.
Cost and operational complexity - Does your team want to run and maintain vector stores, embedding pipelines, rerankers, and versioned documents?
Behavioural control - Are you solving for what the model knows, or for how it consistently behaves and formats outputs?
RAG shines when you must incorporate large, external, or frequently changing knowledge at inference time. But it brings retrieval latency, failure cases (bad or missing context), and added complexity. When those downsides outweigh the benefit of dynamic knowledge, explicitly avoid RAG.
