How much data do I need for fine-tuning?

Minimum ~50-100 high-quality examples for simple tasks, 500+ for complex tasks. Quality matters more than quantity. Well-curated examples with diverse coverage outperform larger noisy datasets.

Should I use fine-tuning or RAG?

Use RAG for knowledge that changes, when you need citations, or when you have limited training data. Use fine-tuning for consistent style/format, domain-specific language, or when you need lower latency. Many systems use both.

How much does fine-tuning cost?

Costs vary widely. Cloud API fine-tuning (OpenAI) starts at ~$25/million tokens for training. Self-hosted with LoRA can be done on a single GPU. The custom model then has ongoing inference costs.

What is the difference between fine-tuning and training from scratch?

Training from scratch builds a model from random weights - requiring millions of examples and enormous compute. Fine-tuning starts with a capable model and adjusts it with your specific data - much faster and cheaper.

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Fine-Tuning

Adapting a pre-trained model to a specific task or domain by training it further on specialised data. Creates a new model variant.

In-Depth Explanation

Fine-tuning takes a pre-trained model and continues training it on your specific dataset, specialising its capabilities for your use case. This creates a custom model variant with knowledge and behaviours tailored to your needs.

The fine-tuning process:

Start with a capable pre-trained model (GPT-4, Llama, etc.)
Prepare a dataset of examples in your domain
Train the model on your data (typically for a few epochs)
The model's weights are adjusted to perform better on your specific task
You now have a custom model deployment

When to consider fine-tuning:

Do fine-tune when you need: consistent formatting, domain-specific knowledge baked in, particular tone/style, reduced prompt length, higher throughput
Don't fine-tune when: prompting achieves good results, data changes frequently, you lack sufficient training examples (<100), or RAG can provide the knowledge

Modern efficient techniques (LoRA, QLoRA) have made fine-tuning much more accessible, reducing compute requirements by 90% or more.

Business Context

Fine-tuning is best when you need consistent, specialised behaviour that can't be achieved through prompting alone. Consider it for high-volume, specialised tasks.

How Clever Ops Uses This

We help Australian businesses determine when fine-tuning is worth the investment versus using RAG or advanced prompting. Often, clever prompting achieves 80% of fine-tuning benefits at 10% of the cost.

Example Use Case

"Fine-tuning a model on your company's writing style for consistent brand voice across all AI-generated content."

Frequently Asked Questions

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