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

Every LLM tuning method compared: when to prompt, when to LoRA, when to go full fine-tune.

## LLM Tuning Methods Explained 2026 "Tuning" means different things. Here are the four methods in order of increasing cost and power. ### 1. Prompt tuning / in-context learning Add instructions and examples to the prompt. Zero training, zero GPU. Best for: style changes, simple formats. Limit: does not teach new knowledge. ### 2. RAG (retrieval augmentation) Attach relevant documents at query time. No training needed. Best for: up-to-date facts, private data. This is the 2026 default for knowledge tasks. ### 3. LoRA / QLoRA (parameter-efficient) Train small adapter matrices (0.1-1% of parameters). 1-2 hours on one GPU for 7B. Best for: domain style, tool-calling formats, specific outputs. QLoRA adds 4-bit quantization to fit bigger models. ### 4. Full fine-tuning Update all weights. Requires 4-8x the VRAM of LoRA and careful regularization. Best for: new languages, major capability shifts, distillation. ### Decision guide | Goal | Method | |------|--------| | Teach new facts | RAG | | Change tone/format | Prompt tuning or LoRA | | Learn a domain deeply | LoRA | | New language | Full fine-tune | ### Cost comparison (7B model, one run) - Prompt tuning: 0 USD - RAG: 0 USD (just compute for embeddings) - LoRA: ~1-3 USD on cloud GPU rental - Full fine-tune: ~10-30 USD on cloud GPU ### 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.