Fine-Tuning vs RAG vs Prompting: Which Approach to Use?
By Karthick Selvam · Published
Three common ways to adapt a language model to your needs, each suited to different problems.
Prompting
Write better instructions and examples. Cheapest and fastest; try it first.
Retrieval-augmented generation (RAG)
Retrieve relevant documents at query time and give them to the model. Best when answers depend on your own, changing knowledge and you need citations.
Fine-tuning
Further train a model on example inputs and outputs. Best for consistent style, format or specialised behaviour, not for adding frequently changing facts.
Decision guide
Start with prompting, add RAG for knowledge, and consider fine-tuning only when you have good training examples and a measurable gap. They can be combined.
Frequently asked questions
Is fine-tuning expensive?
It needs curated data, evaluation and maintenance; costs vary widely by model and approach.
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