RAG vs Fine-tuning vs Prompt Engineering vs LoRA
LLM Customization Method Comparison
Comparison of 4 LLM customization approaches — cost, latency, accuracy, maintenance, and best use cases.
RAGFine-tuningPrompt Eng.LoRA
Full Specification Comparison
| Specification | RAG | Fine-tuning | Prompt Eng. | LoRA |
|---|---|---|---|---|
| Setup Cost | Low | High | Very low | Medium |
| Latency | +200-500ms | Baseline | Baseline | Baseline |
| Knowledge Updates | Real-time | Requires retrain | Manual | Requires retrain |
| Accuracy (domain) | High | Very high | Moderate | High |
| Hallucination Risk | Low | Moderate | Higher | Moderate |
| GPU Required | For embeddings | Yes (training) | No | Yes (light) |
| Best For | Dynamic knowledge | Domain language | Quick tasks | Budget fine-tune |
Expert Verdict
RAG is best for dynamic knowledge; fine-tuning for domain-specific language; prompt engineering for quick wins; LoRA offers efficient fine-tuning at 1% of parameters.
Subject Breakdown
RAG
2 Category Wins
- ★ Knowledge Updates: Real-time
- ★ Hallucination Risk: Low
Fine-tuning
2 Category Wins
- ★ Latency: Baseline
- ★ Accuracy (domain): Very high
Prompt Eng.
2 Category Wins
- ★ Setup Cost: Very low
- ★ GPU Required: No
LoRA
0 Category Wins