Home / Compare / RAG vs fine-tuning: which should you choose?
Deepskilling Compare
Should I use RAG or fine-tuning for my LLM application?
Use RAG to ground answers in fresh, citable knowledge. Use fine-tuning to change model behaviour, format, or skills. Most production GenAI systems need strong retrieval first; add adapters when behaviour gaps remain.
RAG
- Knowledge stays outside weights and can be updated continuously
- Supports citations and source-level access control
- Fails gracefully when retrieval is empty if you design refusals
- Cheaper iteration for document-heavy enterprise Q&A
Fine-tuning (incl. LoRA/QLoRA)
- Encodes style, schemas, and tool-use habits into the model
- Can reduce prompt length for repetitive behaviours
- Requires curated datasets, eval suites, and versioning
- Does not replace a knowledge base for changing facts
Choose RAG when
- Documents, policies, or tickets change often
- Auditors need evidence of sources
- You are early and still discovering user questions
Choose Fine-tuning (incl. LoRA/QLoRA) when
- The base model ignores required formats even with good prompts
- You need domain voice or specialized reasoning patterns
- You can maintain labelled examples and regression evals
VERDICT
Default to RAG with evaluation harnesses. Add LoRA/QLoRA when behavioural gaps are stable and measurable. Combine both for copilots that must sound on-brand and stay factual.