Fine-tuning vs RAG: When to Fine-tune, When to Use RAG

RAG Use Cases

Frequently updated knowledge (documents, FAQs, product info); need for real-time data; need for explainability (can point to answer sources); rapid prototyping.

• RAG advantages: no training needed, low cost, real-time updates, traceable answers
• RAG disadvantages: retrieval quality affects answer quality, context length limits

Fine-tuning Use Cases

Need to change model's behavior pattern or speaking style; large volumes of similar tasks need batch optimization; need to internalize domain-specific reasoning patterns.

• Fine-tuning advantages: stable results, faster inference
• Fine-tuning disadvantages: high training cost, no real-time updates, requires large labeled datasets

Decision Tree

Does knowledge change frequently? → Yes → RAG. Need to cite sources? → Yes → RAG. Need to change model style? → Yes → Fine-tuning. Can both be combined? → Yes, use RAG retrieval + fine-tuned model generation.