Fine-Tuning vs. Prompting vs. RAG: When to Use Each

Your technical team is going to ask this question soon (if they haven’t already): “Should we fine-tune a model?” And 9 times out of 10, the answer is no — not yet. Let me explain the three approaches and when each one makes sense.

Prompting (Start here. Always.) What it is: Writing good instructions for an off-the-shelf model. No custom training. No infrastructure.

• Cost: Near zero (just API usage)
• Setup time: Minutes
• Best for: 90% of use cases. Writing, analysis, Q&A, brainstorming, data processing
• Limitation: The model doesn’t know anything about your specific company or domain beyond what you put in the prompt

RAG — Retrieval-Augmented Generation (The sweet spot for most companies) What it is: Giving the AI access to your documents at query time. We covered this on Day 15.

• Cost: Low-medium (vector database + API calls)
• Setup time: Days to weeks
• Best for: When you need the AI to answer questions about your specific data — company docs, product specs, policies, customer history
• Limitation: Only as good as your documents. If the answer isn’t in your data, RAG can’t find it

Fine-Tuning (The heavy artillery — use sparingly) What it is: Actually retraining the model on your data so it learns your company’s patterns, terminology, and style.

• Cost: High (compute + data preparation + ongoing maintenance)
• Setup time: Weeks to months
• Best for: When you need the model to deeply internalize your domain — your company’s writing style, your industry’s specific terminology, your product’s technical language at a level that prompting can’t achieve
• Limitation: Requires clean training data, ML expertise, and ongoing maintenance. The model can also “forget” general capabilities if fine-tuned poorly

The decision tree: Start with prompting. If prompting isn’t enough because the AI needs your data → add RAG. If RAG isn’t enough because the AI needs to deeply learn your domain’s patterns → consider fine-tuning. Most mid-market companies never need to go past RAG.