---
title: "AI Sample Dataset Generator"
canonical: https://trickest.com/library/ai-sample-dataset-generator
description: "Turn a topic, row count, and optional schema into a synthetic sample dataset with JSONL, CSV, and a Markdown data dictionary."
---

# AI Sample Dataset Generator

## Overview

Prototype teams often need tabular data before a source system or approved dataset is available. Handwritten rows drift in shape and miss fields that a test or interface needs. This workflow gives you a synthetic dataset in JSONL and CSV, plus a data dictionary for prototypes and tests. Seed Topic defines the subject and constraints, Generate Dataset creates a coherent schema and sample rows, and Finalize Dataset writes the artifacts. Keep the graph with your other [visual workflows](/platform/workflows) when you need to regenerate the fixture from a reviewed brief. It creates illustrative records, not company enrichment or facts about real entities.

## Pipeline

1. **Seed Topic** (`seed-topic`, script) sets the topic, requested row count, and optional schema constraints.
2. **Generate Dataset** (`generate`, agent) turns the brief into typed columns, synthetic rows, and a data dictionary.
3. **Finalize Dataset** (`finalize`, script) writes matching dataset files and a Markdown data dictionary.

## Inputs

- **Dataset brief.** The topic and intended use for the synthetic records. Default: use the example brief in Seed Topic, then replace it before a production test.
- **Row count.** The number of sample records requested from the agent. Default: keep the count configured in Seed Topic for a compact fixture.
- **Schema constraints.** Column names, types, or a data dictionary seed the output must follow. Default: leave this blank so the agent designs the schema from the brief.

## Outputs

- **JSONL dataset.** One synthetic record per line for downstream tests and imports.
- **CSV dataset.** The same records and columns in spreadsheet-ready form.
- **Markdown data dictionary.** The schema and each field definition in a reviewable reference.

## Integrations

- AI

## Sample output

Illustrative output from `finalize`. These records show the artifact shape and do not describe real equipment, companies, or events.

`dataset.jsonl`

```jsonl
{"asset_id":"SYN-001","asset_type":"air-quality-sensor","maintenance_state":"scheduled","inspection_window":"2026-Q4"}
{"asset_id":"SYN-002","asset_type":"temperature-sensor","maintenance_state":"review","inspection_window":"2027-Q1"}
```

`dataset.csv`

```csv
asset_id,asset_type,maintenance_state,inspection_window
SYN-001,air-quality-sensor,scheduled,2026-Q4
SYN-002,temperature-sensor,review,2027-Q1
```

`data-dictionary.md`

```markdown
| Column | Type | Meaning |
| --- | --- | --- |
| asset_id | string | Synthetic record identifier |
| asset_type | string | Illustrative equipment class |
| maintenance_state | enum | Scheduled maintenance state |
| inspection_window | string | Illustrative review period |
```

## FAQ

### Can this workflow model any tabular topic?

Use subjects that fit rows and columns, such as product catalogs, sensor readings, or survey responses. A specific brief produces a more useful schema than a broad topic name.

### Can the agent generate large datasets?

Set the requested count in Seed Topic. Keep each request small enough for one agent response, then split larger fixtures into reviewed batches.

### Is the generated data safe to use in production?

The records are synthetic and intended for prototypes and tests. Do not treat them as observed facts or use them as a source for production decisions.

### Can I control the schema of the output?

Yes. Add column names, types, or a data dictionary seed in Seed Topic. Leave those constraints blank when you want the agent to propose the schema from the dataset brief.

## Related workflows

- [Company Enrichment from Website](/library/company-enrichment-from-website) instead when your inputs are real company homepages and you need source-based company attributes.
- [Scrape and Summarize Webpages](/library/scrape-and-summarize-webpages) instead when you need concise reading output from real pages rather than synthetic rows.
- [Daily Tech Digest](/library/daily-tech-digest) instead when you need a recurring briefing from public feeds rather than generated test data.

---
_Markdown source of https://trickest.com/library/ai-sample-dataset-generator._