When to Fine-Tune vs RAG

12 min read

The Core Decision

Fine-tuning and RAG (Retrieval-Augmented Generation) are complementary techniques, not competitors. The right choice depends on what problem you are actually trying to solve.

Use RAG When

Your knowledge base changes frequently (product docs, support articles, live data)
You need to cite sources or show where answers came from
You want to augment a general model without training costs
Your use case requires retrieval of specific documents or records
Latency from retrieval is acceptable

Use Fine-Tuning When

You want the model to adopt a specific writing style or tone permanently
You have a narrow, well-defined task where format consistency matters (JSON output, code generation in a specific framework)
You want to reduce prompt length by baking instructions into the model weights
You need domain-specific reasoning, not just domain-specific facts
Inference latency from retrieval is unacceptable

💡

RAG is like giving your employee a reference manual to look up before answering. Fine-tuning is like training them so thoroughly they just know the answer.

The Combined Approach

The best production systems often combine both: a fine-tuned model for style and task-specific behaviour, plus RAG for dynamic factual retrieval. Fine-tune on behaviour; retrieve for facts.

Start with prompting, then RAG, then fine-tuning. Each step adds complexity. Only move to fine-tuning if prompting and RAG have genuinely failed to solve the problem.

How LoRA and QLoRA Work