Fine-Tuning

Fine-tuning trains a pre-trained AI model further on your own data. Afterward, it knows your domain language, matches your tone, and solves specialized tasks better than the base model. Sounds tempting. But it is the last step, not the first.

Three ways to adapt a model

Prompt engineering changes only the input. Fast, cheap, flexible — often sufficient for 80% of use cases. RAG feeds the model with external documents at runtime. Ideal when current data is critical.

Fine-tuning goes further: it modifies the model's weights. Necessary when consistent behavior in a domain is required and prompting hits its limits. A financial services client needed to classify contract clauses. Prompt engineering: 74% accuracy. After fine-tuning: 93%.

What it costs — and when it pays off

GPT-3.5 with 15 million tokens: around $120. Open-source models like Llama with LoRA adapters: $300-3,000. Full fine-tune of a 40-billion-parameter model: $20,000-35,000.

Our advice: start with prompt engineering. Test RAG. Fine-tune only when both fall short. Most companies that reach out to us do not need fine-tuning — they need better prompts and cleaner data.