System prompts plus few-shot examples solve more than teams expect, at zero infrastructure. Exhaust this before anything fancier; it's also your baseline for measuring the fancier things.
Knowledge changes; retrieval stays current without retraining. If the failure mode is 'the model doesn't know our stuff,' that's RAG, full stop.
Style, format, and high-volume classification — where consistent shape matters and examples abound. Fine-tuning does not reliably add knowledge; using it for that is the classic misfire.
Ask what's missing: behavior → prompt; facts → RAG; form → fine-tune. Combine when justified by measurements, not ambition.
Skipping the discipline this article describes until an incident, audit, or stalled project forces it — every practice above is cheaper adopted early than retrofitted under pressure.
Let's discuss how we can help you with fine tuning vs rag vs prompting.