All resources
Data & RAG20 October 2026·6 min read

RAG, fine-tuning or a long context window? Choosing by the problem

Three techniques solve different problems. Matching the technique to the need avoids expensive detours.

These approaches are often presented as competitors. They address different needs, and many systems use more than one.

Match the technique to the need

  • RAG — when answers must come from current, changing, citeable documents, with access control.
  • Fine-tuning — when you need consistent style, format or behaviour on a narrow task, rather than new facts.
  • Long context — when a bounded set of material fits in one request and the task is occasional.

Questions that decide

  • How often does the information change?
  • Do users need citations?
  • Do different people have different access rights?
  • How much does each request cost at your volume?

Start with the simplest option that meets the requirements and measure it. Move to something heavier only when a measured gap remains.

Related capability

This is what our AI Data Strategy & ML practice is built around.

See how it's scoped