I fine-tuned an LLM for a client, then told them not to use it
A developer fine-tuned GPT-4 for a landscaping company to match their brand voice, but ultimately advised against its deployment. While the fine-tuned model adopted a warmer and more ownership-taking tone, it also confidently hallucinated a warranty that did not exist. The developer concluded that the risk of such fabrications, especially with a small training dataset, outweighed the benefits of the improved voice. AI
IMPACT Highlights the risks of fine-tuning with small datasets, cautioning against deployment when hallucinations could create liabilities.