Fine-Tuning
Also known as: fine-tune, supervised fine-tuning, SFT.
In plain English
A base model like Llama 4 or GPT-4o mini knows general language; fine-tuning teaches it your specific task — classifying support tickets, writing in a house style, or extracting fields from invoices. You supply hundreds to thousands of input/output examples, and a short training run nudges the weights toward that behavior. Modern fine-tuning usually uses parameter-efficient methods (LoRA / QLoRA) that train a small adapter instead of the full network, cutting cost from thousands of dollars to tens. Fine-tuning trades flexibility for consistency: a fine-tuned model is cheaper per call and more reliable on its narrow task, but it can't easily learn new facts (use RAG for that) and it goes stale as your data shifts. In 2026 most teams reach for prompting and retrieval first, and fine-tune only when they need a specific format or tone at scale.
Example
A fintech team fine-tunes GPT-4o mini on 3,000 examples of transaction descriptions mapped to accounting categories. The fine-tuned model classifies new transactions with 96% accuracy at a fraction of the cost of prompting a frontier model with a long instruction on every call. It runs faster too, because the prompt no longer needs pages of examples — the behavior is baked into the weights.
Fine-Tuning vs RAG
Fine-tuning bakes new behavior into a model's weights and is best for teaching a consistent style or format; RAG leaves the weights untouched and injects fresh facts at query time. Fine-tuning changes how a model responds; RAG changes what it knows. For up-to-date knowledge use RAG; for a fixed tone or output schema at scale, fine-tune.
Fine-Tuning in Vincony
Vincony doesn't ask you to fine-tune. Its Prompt Library plus Knowledge Graph memory give most teams the customization they'd want from fine-tuning — house style, reusable instructions, and grounding on your own documents — without a training pipeline or a model to maintain.
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