WHEN TO MOVE BEYOND PROMPTING TO FINE-TUNING

Summary from the TLDR Founders newsletter

Fine-tuning is like giving your AI a specialized degree — it's the right move when companies have exhausted improvements with prompt engineering or need more precise, tailored outputs to meet high customer expectations. For example, Harvey, a legal tech startup, used fine-tuning to boost its AI's accuracy by 15-20% for complex legal language. While it comes with higher costs, fine-tuning becomes essential when you need the reliability and precision that general models can't provide, like accurately interpreting contracts or handling sensitive financial data.