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Fine-Tuning

Further training an existing model on a narrower, task-specific dataset so it performs better on your particular use case, tone, or domain.

Implementation

A foundation model is trained to be generally competent across a huge range of tasks. Fine-tuning takes that general model and trains it further on a smaller, focused dataset, your support tickets, your legal contract language, your brand’s tone, so it becomes noticeably better and more consistent at that specific job.

It’s a heavier, slower, more expensive lever than prompt engineering, and it’s the wrong first move for most problems. If the model gets the right answer once you give it the right documents, that’s a retrieval problem, not a fine-tuning problem. Fine-tuning earns its cost when the issue is consistency of style or judgment across thousands of cases, not lack of a specific fact.

The dataset quality is what determines the outcome, not the fine-tuning process itself. A model fine-tuned on inconsistent or low-quality examples will confidently reproduce those inconsistencies at scale.

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