LLM Fine-Tuning vs RAG: When to Use Which
A practical decision framework for choosing between fine-tuning and RAG for your AI use case — with cost and performance data.
LLM Fine-Tuning vs RAG: When to Use Which
Teams building AI features face a fundamental choice: fine-tune a model on your data, or use RAG to feed relevant context at query time. Each approach solves a different problem.
When to Fine-Tune
Fine-tuning modifies the model's weights to internalise patterns from your training data. Use it when:
- ›Consistent output format: You need the model to always output structured JSON with specific fields
- ›Domain language: Legal, medical, or financial terminology that general models handle poorly
- ›Latency sensitivity: Fine-tuned models don't need context injection, saving 200-500ms per query
When to Use RAG
RAG retrieves relevant documents at query time and injects them into the prompt. Use it when:
- ›Dynamic knowledge base: Documents change frequently (legal updates, product catalog, support articles)
- ›Auditability: You need to cite specific sources for every answer
- ›Cost sensitivity: RAG with gpt-4o costs ~₹0.50 per query vs ₹2+ for a fine-tuned model of similar quality
Hybrid Approach
For most Mumbai enterprise use cases, start with RAG: it's faster to implement, cheaper to maintain, and provides source citations. Add fine-tuning later if you need consistent output formatting or latency improvements beyond what prompt engineering can achieve.
Related: Building a RAG system, AI resume screening.