Fine-tuning vs RAG vs prompt engineering
Not sure which path to take? Seven questions. We will tell you when to stick with prompts, when to retrieve docs, and when fine-tuning is worth the bill — and when it is not.
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Sweet spot
When not to use it
Rough cost
Time to ship
Anti-recommendation
Hybrid hint
Runner-up
Why not the others?
About this tool
This is rule-based scoring, not magic. It nudges you toward the approach that matches data freshness, corpus size, volume, budget, latency, and team skills — then spells out what each path costs in time and money.
Most teams should start with prompt engineering, add RAG when facts live outside the window, and fine-tune only with evals and stable examples. The questionnaire encodes that bias on purpose.
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