RAG vs Fine-Tuning: Choosing the Right Approach for Your LLM App

The short answer

For most business applications — assistants over your documents, support bots, extraction pipelines — RAG (retrieval-augmented generation) is the right first move. It is accurate, cheap, updatable and easy to secure. Fine-tuning shines when you need a specific style, format or deep domain behavior that retrieval cannot provide.

When RAG wins

RAG grounds the model in your actual documents at query time. When policies change, you update the document — not the model. It also gives you citations, which compliance and legal teams love.

• Knowledge bases, policies, manuals, tickets, wikis
• Data that changes often
• Use cases needing source citations and auditability
• Access control per user / per document

When fine-tuning wins

Fine-tuning changes the model's behavior itself: a consistent voice, strict output formats (JSON, SQL), domain jargon, or reasoning patterns in a specific field. It is slower and costlier to update, so use it for stable, high-volume behavior.

• Brand voice and tone across all outputs
• Structured output formats (JSON schemas, SQL)
• Domain-specific reasoning (legal, medical, financial)
• Small, high-frequency tasks where latency/cost matter

How we decide on client projects

We start with prompting + RAG and build an evaluation set of real tasks (a 'golden dataset'). Only when accuracy plateaus do we add fine-tuning — and we re-run the same evaluations to prove it helped. This evidence-first approach avoids expensive tuning that doesn't move the metrics.