Fine-Tuning

Fine-tuning retrains a model's parameters on a large set of examples so it consistently responds in a specific tone or performs more accurately on a narrow task.

Plenty of companies hear 'custom model' and jump straight to fine-tuning, assuming it's the only way to get something truly tailored — but it's actually an advanced option, not a starting point, and jumping in too early often burns budget without a matching payoff. Fine-tuning suits a use case that has already stabilized with enough consistently labeled data, for example a document-classification task that has run smoothly for six months and accumulated thousands of human-verified answers, with a clear sense of what actually needs adjusting. A common misconception is expecting fine-tuning to make up for too little data — in practice, an AI agent typically needs 1-3 months after launch before its efficiency or cost gains become clear, and the data gathered in that window usually isn't enough to support a meaningful fine-tune yet, so jumping in early just wastes budget and delays launch. When data is thin or the use case is still shifting, fine-tuning's ROI is usually low; using RAG first to wire up the knowledge base and accumulate real feedback while the system runs is typically the more practical, cheaper starting point — fine-tuning is worth evaluating once things settle, not treated as a default line item from day one.