Should I use RAG or fine-tuning for my AI product?
Use retrieval-augmented generation when the system needs relevant external knowledge at response time. Consider fine-tuning when you need to adapt model behavior through training examples. They address different problems and can be combined; first identify whether the failure is missing evidence, unclear instructions, or task behavior.
What does the source say?
Google describes RAG as combining retrieval with generation, while its fine-tuning material describes adapting a foundation model through additional training. Neither technique automatically repairs incorrect source material or a poorly chosen product task.
How can you apply this?
Collect a few failed outputs and label the cause of each. For missing current evidence, test a source-grounded answer. For repeated format or task-behavior problems, test clearer instructions and representative examples before training. Compare approaches on the same tasks, including an out-of-scope question that should remain unanswered.
What should you watch out for?
Do not choose an architecture from a buzzword comparison alone. Include privacy, update frequency, operating cost, and the effort needed to evaluate the change.