generative-ai

RAG vs Fine-Tuning: Which One Canadian Enterprises Actually Need

June 17, 2026 · 5 min read · Altaris AI Academy

Start with the question, not the technique

Most teams pick RAG or fine-tuning based on what their favourite blog post said last month. Wrong order. Pick based on what changes.

Use RAG when knowledge changes

If the ground truth updates weekly (policies, product data, regulations) RAG wins. You control the corpus, you can audit what the model saw, and you can revoke a document without retraining.

Use fine-tuning when behaviour changes

If you need a specific tone, output format, or reasoning pattern that no prompt can reliably force, fine-tune. Canadian teams underuse this because they overestimate cost.

The compliance angle

RAG is easier to defend under PIPEDA and AIDA because provenance is inspectable. Fine-tuning bakes data into weights — deletion requests are harder to honour. Plan for it before you promise anything.

The pragmatic default

Start with RAG. Add fine-tuning only when telemetry proves prompt engineering has plateaued.