Somewhere in the first week of any AI assistant project, someone asks whether you should fine-tune a model on your content instead. It sounds like the more serious option.
It usually is not, and the reason is simple once the two are put side by side. Fine-tuning changes how a model writes. Retrieval changes what it can read at the moment it answers. Most teams asking the question have a reading problem.
Answer three questions
Your situation decides it, not the technique. Pick a yes or a no for each.
Notice how rarely fine-tuning alone comes out ahead. It needs stable facts, no demand for a source, and a problem that is purely about style. That combination exists, but it is not what a support assistant or a documentation search looks like.
What each one actually changes
The third row is the one that ends most discussions. An assistant that cannot show its source cannot be checked, and one that cannot be checked is hard to put in front of customers.
Why fine-tuning on your help centre goes wrong
The appeal is understandable: train the model on everything you have written and it will simply know your business. What you get instead is a snapshot.
The day a price, a policy or a feature changes, the model is out of date, and nothing on the page tells the reader so. Fixing it means another training run. Meanwhile the facts are spread through the model's weights, so there is no document to correct and no passage to show when a customer asks where an answer came from.
Retrieval keeps the facts in documents, which is where your team already maintains them. Change the document, and the next answer changes with it.
welcomeai.devWhat is RAG? Retrieval-augmented generation explainedWhere fine-tuning genuinely earns its place
It is the right tool when the problem really is the writing, and nothing else has fixed it: a strict output structure, a register the model keeps drifting out of, a classification it has to make the same way every time.
Before training anything, write the instruction you would give a new colleague: the format, the tone, what never to say. Then test it on real questions. A clear instruction on a capable model solves most of what people reach for fine-tuning to fix, and it can be changed in a minute.
When both problems exist, they stack in a clear order: retrieval for the facts, instructions for the voice, and fine-tuning only if a consistent shape still will not hold.
How WelcomeAI approaches it
WelcomeAI answers by retrieval. Your pages, documents and question-and-answer pairs are indexed, the best passages are handed to the answering model, and the answer cites them. You choose the answering model per knowledge base, and larger models follow instructions more exactly and are more willing to say the content does not cover something.
What the model choice does not change is what was retrieved. If the right passage never reached the model, no model can answer from it, which is why the passages are worth reading before the model is worth changing.
welcomeai.devModels, and which one to useQuestions people ask
If you are deciding this week, start with retrieval and a clear instruction, and keep fine-tuning in reserve for the day a formatting problem survives both.
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