Shelf three · Language models & generative AI
Fine-tuning & Adapters
Kitchen table
The machine already speaks – years of reading taught it that. Fine-tuning is like teaching it your family's way of speaking: your jokes, your rules, how you sign your letters. You do not send it back to school. You give it a short course of examples until it picks up the style. But if you want it to know today's facts, a short course is the wrong tool. You would hand it the facts to read instead. Style is taught. Facts are looked up.
Whiteboard
LoRA and the PEFT family: small trainable deltas over frozen weights, so you tune millions of parameters instead of billions and swap adapters per task. Excellent for form, tone, format compliance and domain vocabulary. A poor and expensive way to install facts, which go stale and are better retrieved at inference. The classic failure is fine-tuning on data you could have put in context – spending training-money on a retrieval problem. Evaluate against the base model plus good prompting first. The delta is often smaller than the meeting that approved it.
Boardroom
Use it when the model must reliably sound like you or follow your formats – brand voice, document conventions, regulated phrasing. If the goal is 'know our data', retrieval is usually cheaper, faster to update and easier to audit. Fine-tuning bakes knowledge in like print, retrieval keeps it loose-leaf. The questions that sort most proposals: how often does this knowledge change, and can we produce the thousands of good examples tuning needs? Style changes rarely. Facts change weekly. Match the tool to the shelf-life.
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