Fine-Tuning vs RAG vs Prompting for AI Apps

Choose the Simplest Technique That Works

Engineers often compare prompting, retrieval-augmented generation, and fine-tuning as if one is always best. In production, the right choice depends on the task, data freshness, evaluation results, latency, cost, and maintenance burden.

When Each Pattern Fits

• Prompting: good for instructions, formatting, reasoning patterns, role behavior, and low-complexity workflows.
• RAG: good when answers depend on private, changing, or permissioned knowledge sources.
• Fine-tuning: useful when the model needs consistent style, domain behavior, classification patterns, or specialized output behavior that prompting cannot reliably achieve.
• Hybrid: many systems use prompting plus retrieval, then fine-tune only when evals justify it.

Let Evals Decide

Do not choose the most advanced approach because it sounds impressive. Run evals against the real workflow and compare quality, latency, cost, maintainability, and risk.