---
title: "Fine-tuning vs. pre-trained models"
description: "Why most teams should start with a capable pre-trained model, and when fine-tuning earns its cost."
url: https://lidvizion.ai/resources/basics/model-fine-tuning-vs-pre-trained-models/
site: Lid Vizion
index: https://lidvizion.ai/llms.txt
---

[Basics](https://lidvizion.ai/resources/) AI setup

# Fine-tuning vs. pre-trained models

Why most teams should start with a capable pre-trained model, and when fine-tuning earns its cost.

## Start with what exists

Pre-trained models already handle a wide range of language and vision tasks. Clear instructions, good examples in the prompt, and the right context retrieved from your own systems solve most business problems without training anything.

## When fine-tuning helps

Fine-tuning adjusts a model’s weights using your own labelled examples. It tends to pay off when:

- The task is narrow and repeated at high volume.
- The output format or style must be very consistent.
- The domain looks different from public data, such as a specific camera setup or document type.
- You need a smaller, cheaper model to match a larger one on one job.

|  | Pre-trained + context | Fine-tuned |
| --- | --- | --- |
| Time to first result | Days | Weeks |
| Data needed | A few good examples | Hundreds to thousands, labelled |
| Changes | Edit instructions or context | Retrain and re-evaluate |
| Best for | Most workflows | Narrow, high-volume tasks |

## What it costs

Fine-tuning needs a representative, labelled dataset, an evaluation set kept separate from training, and a plan to retrain when conditions change. Measure the pre-trained baseline first so you know whether the extra work actually improved anything.

Put it to work

## Configure your AI

Turn individual AI use into a repeatable way of working.

[How we do it](https://lidvizion.ai/services/ai-setup/)

Keep reading

## More basics.

[All basics](https://lidvizion.ai/resources/)

- [Embeddings and feature vectors](https://lidvizion.ai/resources/basics/embeddings-feature-vectors/): How models turn text, images and records into numbers that can be compared.
- [What is computer vision?](https://lidvizion.ai/resources/basics/what-is-computer-vision/): Software that turns images and video into structured information a system can act on.
- [Image vs. video processing](https://lidvizion.ai/resources/basics/image-vs-video-processing/): When a single frame is enough, and when you need motion, sequence and time.

Bring the messy version

## One workflow. A better way to run it.

Show us the tools, the handoffs, and the part that keeps slowing your team down.

[Let’s map your workflow](https://lidvizion.ai/contact/) · [Read our approach](https://lidvizion.ai/approach/)