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Gong co-founder and chief product officer Eilon Reshef says companies should exhaust prompting and test the economics before fine-tuning a model for a specific job. His practical threshold: it is more likely to pay off at millions of inference calls a month than thousands, especially once ongoing maintenance is counted.
Why it mattersReshef recommends starting with a narrow, repeatable task and a set of human-approved answers to test against. Fine-tuning is an engineering choice, he argues, not a moat by itself; useful data, evaluation and the surrounding product matter more. There is a catch beyond the initial bill: teams take on hosting, monitoring and retraining as models and data change. Reshef’s Forbes Council essay makes a good case for treating specialisation as a measured decision, not an AI-shaped badge.
Discuss: Would you fine-tune for control or quality even when the cost savings are uncertain?
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