RAG vs Fine-Tuning vs Prompt Engineering: The Definitive Decision Framework

After deploying 50+ LLM applications across industries, we developed a decision framework for choosing between prompt engineering, RAG, and fine-tuning that consistently leads to the right architecture.

Three Approaches

Prompt engineering crafts instructions in the context window. RAG retrieves documents from a vector database to ground responses. Fine-tuning modifies model weights with your training data.

Decision Framework

Start with prompting for general tasks at 85%+ accuracy. Use RAG for proprietary, changing data. Fine-tune for specific style or specialized tasks where base models underperform.

Production Benchmarks

Prompting: 75-85% accuracy, zero cost. RAG: 88-94%, moderate overhead. Fine-tuning: 92-97%, ongoing infrastructure. Optimal is often hybrid: fine-tuned model + RAG + engineered prompts.

Recommendations

Start with RAG over GPT-4 or Claude. Fine-tune only with evidence, 1,000+ examples, and management infrastructure. QuantPi.ai runs structured experiments across all approaches before committing.