RAG, fine-tuning, and the discipline of model strategy

The right model architecture is determined by the task, the knowledge lifecycle, and the standard of evidence required.

Retrieval-augmented generation is well suited to work that depends on current organisational information. It brings approved context into the model interaction at the point of use and makes it possible to show the sources behind a response.

A practical starting point

Fine-tuning has a different role. It is appropriate when an organisation needs more consistent domain language, output structure, decision patterns, or task-specific behaviour. It should be based on carefully curated examples and measured through a defined evaluation process.

In mature systems, these approaches are often complementary. Retrieval supplies live and attributable knowledge; model adaptation establishes repeatable behaviour. A sound strategy determines what must be retrieved, what must be learned, and how both will be governed over time.