When to Fine-Tune vs When to Just Prompt: A Decision Framework

Fine-tuning is rarely the right first move; most teams should exhaust prompting + RAG before considering fine-tuning.

What AI does well here

• Try prompt engineering first — well-engineered prompts often match fine-tuning performance at zero cost
• Try RAG second when knowledge or domain context is the gap
• Consider fine-tuning when you have: stable use case, large labeled dataset, latency or cost issues prompt engineering can't solve
• Use LoRA / parameter-efficient methods rather than full fine-tuning when possible

What AI cannot do

• Make a bad use case good with fine-tuning
• Substitute for high-quality training data — fine-tuning amplifies data quality, good or bad
• Eliminate the iteration cost — fine-tuning slows your iteration speed dramatically