> For the complete documentation index, see [llms.txt](https://docs.zingg.ai/zingg/llms.txt). Markdown versions of documentation pages are available by appending `.md` to page URLs; this page is available as [Markdown](https://docs.zingg.ai/zingg/versions/enterprise-v0.9.1/stepbystep/createtrainingdata/findtrainingdata.md).

# Finding Records For Training Set Creation

Pairs of records that match or dont so as to train Zingg

The **findTrainingData** phase prompts Zingg to search for edge cases in the data which can be labeled by the user and used for learning. During this phase, Zingg combs through the data samples and judiciously selects limited representative pairs that can be labelled by the user. Zingg is very frugal about the training so that user effort is minimized and models can be built and deployed quickly.

This **findTrainingData** job writes the edge cases to the folder configured through `zinggDir/modelId` in the config:

`./zingg.sh --phase findTrainingData --conf config.json`

The **findTrainingData** phase is run first and then the label phase is run and this cycle is repeated so that the Zingg models get smarter from user feedback.


---

# Agent Instructions
This documentation is published with GitBook. GitBook is the documentation platform designed so that both humans and AI agents can read, navigate, and reason over technical content effectively. Learn more at gitbook.com.

## Querying This Documentation
If you need additional information that is not directly available in this page, you can query the documentation by asking a question.

Perform an HTTP GET request on the following URL with the `ask` and `goal` query parameters:

```
GET https://docs.zingg.ai/zingg/versions/enterprise-v0.9.1/stepbystep/createtrainingdata/findtrainingdata.md?ask=<question>&goal=<user_goal>
```

`ask` is the immediate question: it should be specific, self-contained, and written in natural language.
`goal` is what the user is ultimately trying to achieve, the reason they need the answer. Sharing it helps GitBook give you a better, more relevant answer. A goal is most helpful when it describes the outcome the user wants rather than restating the question. For example, with `ask=how do I create an API token`, a goal like `automate deployments from our CI pipeline` lets GitBook tailor the answer to that use case.

The response will contain a direct answer to the question and relevant excerpts and sources from the documentation.

Use this mechanism when the answer is not explicitly present in the current page, you need clarification or additional context, or you want to retrieve related documentation sections.