---
language: en
title: "Fine-tuning"
description: "Fine-tuning adapts a previously trained model to a task or domain. Learn when to test this technique and how to evaluate its results."
canonical: "https://tropk.ai/en/resources/glossary/fine-tuning"
last_modified: "2026-10-12T16:13:25.230Z"
---

# Fine-tuning

Reviewed on 2026-10-06.

## What it means

Fine-tuning is a training stage that adapts an already trained model to a task or domain. Instead of starting from scratch, the process uses its parameters as a starting point and learns from a specific set of examples. The result is an adapted version whose quality needs to be evaluated.

## Why it matters for marketing

For marketing, this technique may be worth considering when an assistant needs to maintain recurring classifications, formats, or editorial standards. The team should identify a persistent difficulty and gather reviewed examples before commissioning training. The existence of materials about the brand online is not the same as performing fine-tuning, nor does it demonstrate that a public model has been updated.

## Example in practice

Imagine a network of schools that classifies contact requests as enrollment, transfer, or cancellation. Even with clear instructions, its model confuses transfers between locations with new enrollments. The team sets aside reviewed examples for fine-tuning and reserves new requests for evaluation. It adopts the adapted version only if it reduces this confusion without harming the other categories.

## How to apply or verify

Define the task, the source model, and the success criteria. Separate training and evaluation data, avoiding identical messages in both sets. Compare the adapted version with the initial version under equivalent conditions. The technical team records the configuration and results; those who know the business check for errors relevant to operations.

## Important distinctions

Fine-tuning modifies parameters through training; writing a [prompt](/en/resources/glossary/prompt) provides instructions in the input. [RAG](/en/resources/glossary/rag) adds information retrieved from external sources to generation. These approaches can be combined, but they address different needs. Fine-tuning should not be treated as a guarantee of factual updates or as a way to control recommendations in public services operated by third parties.

## Sources and references

- [Fine-tuning](<https://huggingface.co/docs/transformers/main/en/training>) — Hugging Face
- [Machine Learning Glossary: fine-tuning](<https://developers.google.com/machine-learning/glossary#fine-tuning>) — Google for Developers
- [Retrieval-Augmented Generation for Knowledge-Intensive NLP Tasks](<https://arxiv.org/html/2005.11401v4>) — Lewis e colaboradores / arXiv

## Continue a leitura

- [Training data](</en/resources/glossary/training-data>)
- [LLM](</en/resources/glossary/llm>)
- [RAG](</en/resources/glossary/rag>)

## About the author

[Vinicius Pacheco](</en/team/vinicius-pacheco>)

Responsible for Tropk’s technical content, focusing on crawling, indexing, structured data, and agent access to content.

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