AI Glossary
What it means, why it matters to your business, and where it shows up in a real build decision.
In plain terms
Fine-tuning is further training a foundation model on your own examples so it adopts a specific tone, format, or task. Powerful, but usually the wrong first move. For most business problems, retrieval and prompting are cheaper and far easier to update than a fine-tuned model.
Fine-tuning is taking a highly educated generalist and putting them through a specialist apprenticeship using your own documents, tone, and processes.
A customer support team fine-tunes a model on 10,000 of their own resolved tickets. The result handles tier-1 queries with the company's exact phrasing and escalation logic, without any prompt engineering overhead.
When a specific tone, format, or domain vocabulary is critical and cannot be achieved through prompting alone. Works best when you have hundreds of high-quality examples of the target output.
As a first step. Try prompting and RAG first. Fine-tuning is expensive to set up, requires high-quality labeled data, and is painful to update when your content or policies change.
Higher setup and upkeep cost than prompting or RAG.
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Tell us what's slowing you down and we'll show you where LLM fine-tuning fits.
Work with us
Tell us what's not working in your business. We'll find the real problem and tell you exactly what it would take to fix it.