LLM Tuning Methods Explained 2026: Prompt Tuning vs LoRA vs Full Fine-Tune

Prompt engineering vs RAG vs LoRA vs full fine-tuning: cost, data, and VRAM comparison plus a decision table.

💡 What You Will Learn

Prompt engineering vs RAG vs LoRA vs full fine-tuning: cost, data, and VRAM comparison plus a decision table.

Four ways to make an LLM fit your business, in order of cost: prompt engineering (free, minutes), RAG (documents only, hours), LoRA/QLoRA fine-tuning (hundreds to thousands of examples, 8-24GB VRAM, days), and full fine-tuning (tens of thousands of examples, 24GB+ VRAM, weeks). Prompt engineering handles style and output format. RAG is the default for knowledge tasks - it injects facts that can be updated without retraining, and quality is bounded by retrieval. LoRA trains only 0.1-1% of parameters via adapters (tools: PEFT, LLaMA-Factory, Unsloth); it learns behavior and style, not facts. Full fine-tuning is only worth it for new languages or capability leaps. Common mistakes: fine-tuning to inject knowledge (use RAG instead), assuming more data is better (quality wins), and skipping A/B comparison with the base model. QLoRA works on 8GB cards; style mimicry needs only a few hundred examples.

❓ FAQ

Is RAG a tuning method?

Technically no, but it solves the same user problem, so it belongs in the decision.

Can I combine LoRA and RAG?

Yes - they are orthogonal; use LoRA for behavior and RAG for knowledge.