RAG vs Fine-tuning for business AI - when does each actually make sense? (non-technical breakdown)

I've been helping a few small businesses set up AI knowledge systems and I keep getting asked the same question: "should we fine-tune a model or use RAG?"

Here's my simplified breakdown for non-ML founders:

RAG (Retrieval-Augmented Generation)
- Best when: your data changes frequently (SOPs, policies, product catalogs)
- Lower cost to maintain
- You can update the knowledge base without retraining
- Response quality depends on how well you chunk/embed your docs
- Great for: internal knowledge bots, customer support, HR Q&A

Fine-tuning
- Best when: you want a specific style/tone/format of response
- One-time training cost + periodic retraining cost
- Doesn't keep up with new info unless you retrain
- Great for: copywriting assistants, code assistants with your own patterns

For 90% of businesses, RAG is the right starting point. We've built RAG systems for a logistics company and a coaching brand both saw support ticket volume drop by ~35% within 3 months.

Curious what's your use case? Happy to help people think through the architecture.