RAG vs fine-tuning: how to choose

Start with prompting, add RAG for dynamic knowledge, then fine-tune only when behavior or style constraints remain unsolved.

Use RAG when

• Your source content changes often.
• You need citations and grounded answers.
• You want lower operational risk when content updates.

Use fine-tuning when

• You need highly consistent output style or tone.
• You have quality labeled examples at scale.
• You have strong evals and can monitor regressions.

Cost and maintenance trade-off

RAG shifts complexity to retrieval quality and indexing. Fine-tuning shifts complexity to dataset quality, retraining cycles, and model governance.

Default playbook

1. Prompt baseline with strict eval criteria.
2. Add RAG for knowledge gaps.
3. Fine-tune only after repeated, measured failure modes.

Also evaluate deployment constraints in local models vs cloud models.