Stop guessing. Here's the clear decision framework for choosing between fine-tuning, RAG, and prompt engineering — built from real production deployments in 2026. What Each Approach Actually Does Before the framework: let's be precise. Prompt Engineering → Control behavior through instructions. Model unchanged. RAG → Inject retrieved documents into context. Model unchanged. Fine-tuning → Update model weights with your data. Model changed. Enter fullscreen mode Exit fullscreen mode This distinction matters because mixing up the goal (knowledge vs behavior vs style) leads to the wrong choice. The Comparison You Actually Need Criterion Prompt Eng. RAG Fine-tuning Setup cost $0 Medium High Time to deploy Hours 1–2 weeks 2–8 weeks Real-time data ✗ ✓ ✗ Large doc base △ ✓ ✓ Custom style/persona △ ✗ ✓ Hallucination risk High Low Medium Scalability High High Medium Prompt Engineering: Start Here, Always Use when: task is well-defined, examples demonstrate the behavior, prototype phase, cost is a constraint.…