RAG vs Fine-Tuning: Choosing the Right Tool for LLM Customization

Mon, 12 Jan 2026 00:00:00 +0000

“Should we use RAG or fine-tuning?” is a question teams ask when they need an LLM to work better for their specific use case. It’s often the wrong question — they’re different tools that solve different problems and can be combined.

Getting this wrong is expensive. Fine-tuning a model to “know” information that should be retrieved leads to a model that’s expensive to update and confidently answers with stale data. Implementing RAG when the problem is actually about model behavior leads to a system that retrieves correctly but still behaves wrong.

Fine-Tuning on dplabs — Software Engineering & Technology Consultancy

RAG vs Fine-Tuning: Choosing the Right Tool for LLM Customization