Fine-Tuning
Fine-tuning is further training an existing AI model on your own examples so it adapts to a specific task, tone, or format. It's one way to specialize a general model — though for most business cases, good prompting and retrieval get you there without it.
Why it matters for your business
Fine-tuning can be worth it when you need very consistent, specialized output at scale, but it's often overkill for SMBs. Knowing when it's genuinely needed — versus simpler, cheaper approaches like prompting or RAG — saves real money.
In practice
A firm fine-tunes a model on hundreds of its past proposals so drafts come out already in the firm's voice and structure.
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