AI
Retrieval supplies facts at question time and updates when the documents do. Fine-tuning teaches format and behaviour and fixes it into the model. Use retrieval for what is true and fine-tuning for how output should look.
RAG
Fine-Tuning
Retrieval is the right default for business content.
It cites its source, updates the moment the underlying document changes, and makes wrong answers diagnosable: you can see which passage was used. That traceability is usually worth more than any quality gain from tuning.
Fine-tuning is for form.
Consistent output structure, a specific tone, or a classification task where examples teach the behaviour better than instructions can. It genuinely outperforms prompting for those, and it is the wrong tool for facts.
Fine-tuned facts go stale invisibly.
A price, a policy or a product detail baked into weights keeps being recited confidently after it changes, with no signal that it is outdated. Retrieval has no equivalent failure because it reads the current document.
Cost and maintenance differ too.
Retrieval needs a curated corpus and a good chunking strategy; fine-tuning needs quality training examples, evaluation, and repetition whenever the base model updates. For most business applications, retrieval first and tuning only if a specific quality gap remains.
Yes, and sophisticated systems often do: fine-tuning for consistent output format, retrieval for the facts inside it. Start with retrieval, because it solves the more common problem.
Because the facts are in the weights rather than in a document it reads. They were correct at training time and there is no mechanism telling the model they have changed.
Retrieval supplies facts at question time and updates when the documents do. Fine-tuning teaches format and behaviour and fixes it into the model. Use retrieval for what is true and fine-tuning for how output should look.
You have read what we think. If you want to know what it means for your case specifically, describe it and we will tell you which parts of rag vs fine-tuning actually apply — and which do not.
