Fine-Tuning LLMs for Enterprise — When to Do It, When Not to, and How

“Can we train the model on our data?” The number one question from every client. The answer: it depends. Fine-tuning is powerful, but often expensive and unnecessary.

Fine-Tuning vs. RAG vs. Prompt Engineering¶

• Prompt engineering: Zero cost, immediate results, limited context.
• RAG: Medium effort, dynamic data access, no retraining.
• Fine-tuning: High effort, the model learns your style/domain.

When to Fine-Tune¶

• Specific output format: Proprietary structured output.
• Domain-specific language: Medical terminology, legal jargon.
• Consistent style: Responses that sound like your brand.
• Latency/cost optimization: A smaller fine-tuned model replaces expensive GPT-4.

Practical Workflow¶

OpenAI simplified fine-tuning for GPT-3.5 Turbo. For open-source: LoRA and QLoRA enable fine-tuning on a single GPU. This dramatically reduces hardware requirements.

Start with RAG, Fine-Tune Only When You Must¶

The proven approach: prompt engineering → RAG → fine-tuning. Most projects stop at RAG. And that’s OK.