Fine tuning and retrieval solve different problems. Confusing them can make an AI app more expensive and less reliable than it needs to be.
Use Retrieval for Facts
If the answer depends on documents, policies, prices, product data, or recent information, retrieval is usually the right first step.
Use Fine Tuning for Behavior
If the model needs a consistent tone, format, classification style, or domain-specific pattern, fine tuning may help.
Combine Them Carefully
Many strong systems use retrieval for knowledge and fine tuning for response shape. Keep evaluations separate so you know what improved.
Start with the Cheapest Change
Improve prompts, examples, schemas, and retrieval quality before training a custom model.
A useful rule: retrieval teaches the app what to know; fine tuning teaches it how to behave.
Frequently Asked Questions
It can, but retrieval is usually better for facts that change or need citations.
It can be. Cost depends on data size, model choice, training frequency, and evaluation needs.
Start with prompt improvements and retrieval unless you have a clear behavior gap.