Most AI architecture debates jump too quickly to "RAG or fine-tuning?"
That is the wrong framing.
The better question is: what problem are you actually solving?
My current rule of thumb
Use RAG when the problem is about changing facts, private knowledge, citations, and traceability.
Use fine-tuning when the problem is about behavior, style, repeated task patterns, latency, or teaching the model how to respond.
Where teams get it wrong
A lot of AI systems fail because teams fine-tune when they actually need retrieval, or bolt on retrieval when the real issue is task behavior.
Wrong choice usually shows up as:
- stale answers
- hallucinated confidence
- expensive iteration cycles
- poor explainability
- slow path to production
Hot take: most enterprise AI apps need RAG first, fine-tuning later.
Agree or disagree?
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