> ## Documentation Index
> Fetch the complete documentation index at: https://docs.prettywhale.ai/llms.txt
> Use this file to discover all available pages before exploring further.

# FAQ: Pipeline Generation, Formats, and More

> Answers to the most common questions about PrettyWhale.ai: how it works, what it generates, supported formats, and how to get the most out of the platform.

This page collects the questions the PrettyWhale team hears most often. Whether you are evaluating the platform, setting up your first pipeline, or troubleshooting a specific step, you should find a direct answer below. If something is still unclear, reach out at [contact@prettywhale.ai](mailto:contact@prettywhale.ai).

<Accordion title="What is PrettyWhale.ai?">
  PrettyWhale is an AI-powered platform that generates production-ready data  pipelines from a sample data file. Upload a file, configure your pipeline settings, and PrettyWhale.ai produces ingestion code, unit tests, data quality checks, schema definition, and pipeline documentation. It eliminates the repetitive, error-prone manual work of writing ingestion code from scratch.
</Accordion>

<Accordion title="How is this different from Cursor, Claude Code, or Github Copilot?">
  PrettyWhale.ai is really different from copilots. Those tools work inside your editor and answer the question you are typing. They are good at it but they need you as a guide. The unit they produce is a suggestion, and an engineer decides what to do with it.

  [PrettyWhale.ai](http://PrettyWhale.ai) produces the unit above that one: a project you can run, with the tests that prove it runs and the documentation that lets someone else maintain it. Copilots suggest. Engineering AI delivers.
</Accordion>

<Accordion title="What file formats does PrettyWhale.ai accept?">
  PrettyWhale.ai currently accepts **JSON** and **CSV** sample files. For the full list of supported formats, tips on structuring your sample file, and best practices for getting the best generation results, see [Supported Formats](/guides/supported-formats).
</Accordion>

<Accordion title="How does PrettyWhale.ai analyse my uploaded file?">
  After you import a sample file, PrettyWhale.ai runs an automatic **field discovery** pass. It inspects every field in your data: inferring data types, detecting nullability, identifying value patterns, and flagging potential quality issues such as inconsistent formats or out-of-range values. The results appear in the field analysis interface, where you can review each field and choose which fields to include in the generated pipeline before moving on to transformations.

  This discovery step is what allows PrettyWhale.ai to produce accurate schemas and targeted quality rules rather than generic boilerplate.
</Accordion>

<Accordion title="What does PrettyWhale.ai generate?">
  Every pipeline generation produces the same artifacts:

  1. **Executable code**
  2. **Unit tests**
  3. **Output Schema**
  4. **Configurations files**
  5. **Pipeline documentation**

  All  artifacts are generated together and are consistent with one another. You do not need to write or align them manually.
</Accordion>

<Accordion title="Is PrettyWhale.ai a general-purpose AI coding tool?">
  No. PrettyWhale.ai is not a general-purpose code assistant or conversational AI. It uses a proprietary **Small Language Model (SLM)** that has been fine-tuned specifically for data engineering tasks. That specialization is what makes the generated code consistent, validated, and production-grade. You will not use it to write a web server or a React component for example.  It is purpose-built for ingestion pipelines, and that focus is intentional.
</Accordion>

<Accordion title="How long does it take to generate a pipeline?">
  Discovery and Transformations stages typically complete within seconds after you upload your sample file and configure your pipeline settings.

  The exact complete duration depends on the size and complexity of your sample file, but for most standard inputs you will have all  artifacts ready  in minutes.
</Accordion>

<Accordion title="Is the generated code run before I get it">
  Yes. At the end of the generation cycle, the pipeline is executed on our infrastructure before it is packaged. It runs on the sample you provided and the tests are also executed. Thus, what lands in your repository has already been executed.

  That step is what separates our generated pipelines from a copilot’s suggestion. A suggestion is first judged on whether it looks right. Our pipelines are judged on whether they run, and we would rather find that out before you do.
</Accordion>

<Accordion title="Where can I get help or contact support?">
  You can reach the PrettyWhale.ai team at [contact@prettywhale.ai](mailto:contact@prettywhale.ai) or through the contact form at [prettywhale.ai/contact](https://prettywhale.ai/contact).
</Accordion>

<Accordion title="Is my sample used to train your model?">
  No. Your sample is used to generate your project and nothing else. It does not become training material, for us or for anyone else.
</Accordion>