LLM Best Practices

Fine-tuning

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Overview

Fine-tuning continues training a pre-trained model on a curated set of input and output examples so its weights adapt to one task, format, or style. Exhaust prompting, few-shot examples, and retrieval first; fine-tuning costs more to build and maintain.

Fine-tune when a format or tone must hold across very high volume and prompting is inconsistent, when unusual notation defeats the base model, or when a small specialized model must replace a large one for latency or cost. Do not fine-tune to add facts: it does not reliably inject knowledge and can increase hallucination. Parameter-efficient methods (LoRA, QLoRA) train small adapter weights on open-weights models.

Check availability before planning around it. The Claude API does not currently offer fine-tuning, and OpenAI states it is winding down its fine-tuning platform and has closed it to new users. Other hosted options vary by provider; open-weights models with LoRA are the other route.

Build the evaluation-harness and a golden-set first, hold out data from the training distribution, and compare the tuned model against the base model plus a good prompt.

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