Choose the right AI pattern: RAG before fine-tuning

What’s happening Most enterprise use cases are stabilising on retrieval-augmented generation (RAG) with governed knowledge bases. Fine-tuning comes later for niche, high-volume tasks.

Why it matters RAG is faster to ship, cheaper to run, easier to govern, and updates with your content refresh. Fine-tuning locks you into a slower iteration loop unless the use-case demands it.

What to do next week

• Classify candidate use cases: RAG-first vs tune-worthy (high volume, style-critical, structured outputs).

• Stand up a golden source + retrieval layer for one workflow (e.g., policy Q&A, reporting prep).

• Track answer accuracy and time saved, not just model scores.