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Generate a Dataset with Synthetic Data Generation

Synthetic data generation lets you create datasets from various input sources beyond JSONL. You can import documents, scrape websites, process YouTube videos, or combine multiple sources into one dataset.
1

Create a Dataset

Click the + Create Dataset button in the top-right corner of the page.GIF of clicking the create a dataset button
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Select Synthetic Data Option

In the dataset creation modal, select Synthetic data to generate a dataset from your content.
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Step 1: Define Dataset and Sources

  • Enter a descriptive dataset name.
  • Choose one or more data sources:
    • Files Only: PDF, DOCX, TXT, HTML, PPTX
    • YouTube Videos: individual videos or playlists
    • Web Scraping: one or more website URLs
    • Mixed Sources: combine multiple input types
  • Set the number of QA pairs to generate from each source.
Synthetic Dataset Generation - Input sources selection
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Step 2: Set Optional Guidance (Optional)

Get control of the generation process by setting additional parameters:
  • Rules & Constraints – add conditions for the generated content (e.g., enforce style, define tone, restrict scope).
  • QA Guidance – provide example pairs or specify output formats.
  • Creativity Level – adjust the model’s temperature to balance consistency vs. variety.
Synthetic Dataset Generation - Define advanced settings
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Step 3: Review and Create Dataset

Review a recap of your settings from the previous steps. Click Create Dataset to start the generation process.Synthetic Dataset Generation - Summary

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Options for Synthetic Data Generation

When generating synthetic datasets, you can configure:
  • Data Sources – files, YouTube videos, websites, or a mix.
  • Synthetic Pairs Configuration – number of QA pairs per input.
  • Rules & Constraints – optional rules to shape the outputs.
  • QA Guidance – add examples or output specifications.
  • Creativity Level – control the randomness of the generation.
For deeper explanations and advanced usage, see our user guide on how to Generate Dataset from Text Files.

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Next Steps

Create a Snapshot

Create a snapshot of your dataset to save your current state.

Enrich a Dataset

Start a data augmentation job to add synthetic data to your dataset.

Autosplit a Dataset

Automatically split your dataset into training, validation, and test sets.

Fine-Tune a Model

Fine-tune a model on your dataset.