---
language: en
title: "Synthetic data"
description: "Synthetic data are artificially generated examples for training, testing, or analysis. Learn how to assess their usefulness, origin, and limits of representativeness."
canonical: "https://tropk.ai/en/resources/glossary/synthetic-data"
last_modified: "2026-10-12T16:13:29.893Z"
---

# Synthetic data

Reviewed on 2026-10-06.

## What it means

Synthetic data are artificially produced records that represent characteristics, situations, or tasks, rather than directly recording each real-world occurrence. They may include tables, images, and instruction-and-response pairs generated by models. In AI applications, they serve as training or testing material; their usefulness depends on what they preserve and the purpose for which they were created.

## Why it matters for marketing

Marketing teams can use synthetic examples to test classifications and workflows without treating these samples as verified portraits of customers. The relevant decision is identifying which patterns need to be represented. A large dataset that lacks rare complaints or regional language, for example, may produce a reassuring evaluation that does not reflect situations encountered in real customer service.

## Example in practice

A retail chain creates synthetic messages to test the triage of clothing exchanges. In a hypothetical review, it notices that nearly all examples mention online purchases, even though the workflow also handles in-store purchases. The team expands the scenarios and keeps a separate real-world dataset for evaluation, since artificial origin does not ensure coverage of exchange conditions.

## How to apply or verify

When verifying a dataset, record the generation method, sources used, filters, and purpose. Compare categories, combinations, and difficult cases with real data that the team can legitimately use. Keep training examples separate from evaluation examples. For data generated by LLMs, review instructions and responses before incorporating the material into training.

## Important distinctions

Synthetic data are not synonymous with anonymous data: generation may preserve identifiable information or patterns from the sources. NIST distinguishes common methods from techniques with differential privacy guarantees. Synthetic data are also not market observations; invented responses do not prove buyers' preferences. The Self-Instruct study supports one use in [model fine-tuning](/en/resources/glossary/fine-tuning), without guaranteeing gains on any task.

## Sources and references

- [Differentially Private Synthetic Data](<https://www.nist.gov/blogs/cybersecurity-insights/differentially-private-synthetic-data>) — NIST
- [Self-Instruct: Aligning Language Models with Self-Generated Instructions](<https://arxiv.org/abs/2212.10560>) — Yizhong Wang e coautores, arXiv

## Continue a leitura

- [Training data](</en/resources/glossary/training-data>)
- [Fine-tuning](</en/resources/glossary/fine-tuning>)
- [LLM](</en/resources/glossary/llm>)

## 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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