Fine-Tuning vs RAG: Which Should You Use?

They solve different problems. Pick the wrong one and you'll waste time and money.

What Each Does

RAG (Retrieval-Augmented Generation): Give the model access to external documents. Ask about your specific data by providing relevant context as part of the prompt. Fast iteration, no training required, easy to update knowledge.

Fine-tuning: Update the model's weights to change how it behaves. Teaches the model new patterns, tone, or specialized knowledge. Slower iteration, requires training infrastructure, harder to update.

When to Use RAG

• You need up-to-date information (documents, policies, data that changes)
• You have domain-specific documents the model should reference
• You need to cite sources
• You want fast iteration
• Your knowledge base is large (more than fits in a prompt)
• You don't have training data

Use RAG for most applications. It's simpler and faster.

When to Use Fine-tuning

• You want to change the model's behavior or personality
• You need specific response format the model doesn't naturally produce
• You want better accuracy on a narrow task with limited training examples
• You need to improve token efficiency (smaller payload)
• You have high-quality labeled examples

The Common Mistake

Teams fine-tune when they should RAG. Fine-tuning for knowledge (Q&A about documents) is slower, more expensive, and harder to maintain than RAG.

Fine-tune for behavior, style, and format. Retrieve for knowledge.