Fine-Tuning vs RAG vs Prompting: A Decision Framework

"Should we fine-tune?" is the most common question in our AI consulting engagements. It's usually the wrong first question. Here's the framework we actually use.

Start with prompting — always

Well-engineered prompts with few-shot examples solve more problems than teams expect, at zero infrastructure cost. Exhaust this first: structured instructions, output schemas, worked examples. If quality plateaus below requirements, diagnose why before reaching for heavier tools.

Reach for RAG when the problem is knowledge

If failures come from the model not knowing your data — your policies, your products, your tickets — no amount of fine-tuning fixes that efficiently. Retrieval injects current, permissioned, verifiable knowledge. Fine-tuning bakes in a snapshot that's stale by launch day.

Reach for fine-tuning when the problem is behavior

Fine-tuning shines at form, not facts: consistent tone, strict output formats, domain-specific style, or compressing a complex prompt into model weights for high-volume tasks. It's also the path to running smaller, cheaper models at frontier quality on narrow tasks.

The production answer is usually all three

• Prompting for task instructions and guardrails
• RAG for grounding in current enterprise knowledge
• Fine-tuning for high-volume routing, extraction or style-critical outputs

The framework in one line: prompting for instructions, RAG for knowledge, fine-tuning for behavior — and measure everything against an evaluation set before deciding anything.