When Fine-Tuning is Needed

"Should I fine-tune?" is the most common question in AI engineering. The answer is usually "No, fix your prompt first."

What is Fine-Tuning?

Fine-tuning takes a base model (like Gemini 1.5 Flash) and continues training it on your specific data. It updates the internal weights of the neural network.

The Hierarchy of Optimization

1. Prompt Engineering: Cheapest. Fastest.
2. Few-Shot Prompting: Adding examples to the prompt.
3. RAG (Retrieval): Injecting knowledge into the context.
4. Fine-Tuning: Retraining the model.

When to Fine-Tune?

You fine-tune for Form, not Fact.

• Structure/Tone: If you need the model to sound exactly like your company's brand voice (sarcastic, terse, pirate-themed), prompting is hard. Tuning captures "Vibes" perfectly.
• Specialized Format: If you need valid COBOL code or a proprietary JSON schema that the model keeps messing up even with examples.
• Latency/Cost: A fine-tuned smaller model (Flash) can sometimes outperform a prompted larger model (Pro). You tune Flash to replace Pro and save money.

When NOT to Fine-Tune?

• New Knowledge: Do not fine-tune to teach the model "Who won the Super Bowl last week." It is inefficient. Use RAG for facts.
• Reasoning: Tuning rarely improves raw logic capabilities.

Summary

• Try prompting.
• Try RAG.
• If you still need specific style consistency or lower latency: Fine-Tune.

In the next lesson, we discuss Dataset Preparation.