RAG VS FINE-TUNING: PICKING THE RIGHT TOOL FOR THE JOB

The debate comes up in almost every project: should we fine-tune a model on our data, or use retrieval-augmented generation? The framing is usually wrong. They solve different problems.

Fine-tuning changes how a model thinks. RAG changes what it knows. If you need the model to adopt a specific reasoning style, tone, or domain-specific heuristic, fine-tuning is the right tool. If you need it to reference up-to-date, proprietary information it wasn't trained on, RAG is almost always better.

The practical numbers from our projects: fine-tuning a 7B parameter model on a domain-specific dataset costs roughly $80–200 for the initial run, takes 4–12 hours, and needs to be redone every time your data changes significantly. RAG, by contrast, has near-zero setup cost beyond indexing, updates in real time as you add documents, and adds ~200–400ms latency per query depending on your retrieval setup.

Where fine-tuning genuinely wins: tasks where the model needs to internalize a specific output format, classification schemes, or domain jargon that's too voluminous to fit in a context window. Where RAG wins: anything involving live data, large knowledge bases, or compliance-sensitive environments where you need an audit trail of what the model was given.

Our default recommendation: start with RAG. It's faster, cheaper, and more explainable. Graduate to fine-tuning only when you've proven that retrieval quality is the bottleneck.