What the topic means in practice
Fine Tuning changes model behaviour through additional training, while RAG supplies current knowledge when an answer is generated.
When the solution is genuinely useful
It is most useful when the choice follows the knowledge need: RAG supplies current sources while fine tuning changes model behaviour. The use case should follow a concrete problem rather than the mere availability of a new feature.
How to prepare the implementation
Solution quality depends on data quality, segmentation and access control. Define the source of truth, update frequency, versioning and a process for removing outdated information. In practical terms, test a strong prompt and RAG first, then consider fine tuning only for stable examples not achievable by a simpler method.
A boundary that must not be ignored
The main risk is fine tuning is not a dependable factual database, while RAG cannot repair poor documents or imprecise retrieval. Before launch, define decision ownership, escalation rules and a safe way to stop the solution.
How to verify the outcome
For this use case, measure answer accuracy, citations, update cost, format consistency and quality after document or model changes. Compare the result with the previous workflow and review routine cases separately from difficult exceptions.
