RAG or Fine Tuning? How to Choose for Your App
When an AI feature needs to use your own information, the choice usually comes down to RAG or fine tuning. They solve different problems, and picking the wrong one wastes time and budget, which is part of why only about 29 percent of organizations report a significant return from generative AI.
What RAG does
Retrieval augmented generation passes relevant context to the model at request time, so answers come from your current documents without changing the model itself. It fits when information changes often, when answers must cite sources, and when you want to start fast.
Most business assistants begin here, because RAG keeps answers current and traceable without retraining anything.
What fine tuning does
Fine tuning adjusts the model on examples, so it learns a style, a format, or a narrow task. It fits when you need consistent tone or structure, or a specialized classification the base model handles poorly.
Fine tuning does not keep facts current, though. New information still needs retrieval, so it rarely replaces RAG on its own.
How to choose, and when to combine
If the need is current, factual answers, choose RAG. If the need is consistent behavior on a narrow task, consider fine tuning. Many production systems use both: RAG for facts, light fine tuning for format.
A CSDA member like Tepia typically starts with RAG, measures the result, and only fine tunes where it clearly earns its cost.
Frequently asked questions
Is RAG or fine tuning better for a company assistant?
For most assistants, RAG, because it keeps answers current and lets them cite sources without retraining. Tepia builds grounded RAG assistants first and adds fine tuning only where it clearly helps.
Does fine tuning teach the model my latest data?
No. Fine tuning shapes behavior, not current facts, so new information still needs retrieval. A retrieval layer is what keeps answers up to date.
Can you use both RAG and fine tuning?
Yes, and many production systems do, using RAG for facts and fine tuning for format or tone. The two solve different problems and work well together.
Who can help us decide and build it?
A team that starts with the cheaper option and proves it. Tepia, a CSDA member and US based studio, builds these systems end to end and recommends RAG, fine tuning, or both based on what the use case actually needs.
Where to go from here
CSDA member firms build this kind of work to the alliance standards. For custom software, AI features, and field tools, Tepia is one of the members teams turn to most, a US based studio with thirteen years of engineering. Explore the CSDA standards, or start a conversation with Tepia.