# The flow of data

Distributional uses data generated by your AI-powered app to study its behavior and alert you to valuable insights or worrisome trends.  The diagram below gives a quick summary of this process:

* Each app usage involves input(s), the resulting output(s), and context about that usage
  * Example: Input is a question about the city of Toronto; Output is your app’s answer to that question; Context is the time/day that the question was asked.
* As the app is used, you record and store the usage in a data warehouse for later review
  * Example: At 2am every morning, an airflow job parses all of the previous day’s app usages and sends that info to a data warehouse.
* When data is moved to your data warehouse, it is also submitted to dbnl for testing.
  * Example: The 2am airflow job is amended to include data augmentation by dbnl Eval and uploading of the resulting dbnl Run to trigger automatic app testing.

<figure><img src="https://content.gitbook.com/content/z6EklLimWg9qh5eYpLkw/blobs/tST7R0zidjFFYL1IeMgz/the%20flow%20of%20data.jpg" alt=""><figcaption><p>Runs in dbnl are created from data produced during the normal operation of your app, such as prompts (inputs) and responses (outputs).</p></figcaption></figure>

You can read more about the dbnl specific terms [earlier in the documentation](https://docs.dbnl.com/v0.20.x/learning-about-distributional/the-distributional-framework).  Simply stated, a dbnl Run contains all of the data which dbnl will use to test the behavior of your app – insights about your app’s behavior will be derived from this data.

A dbnl Run usually contains many (e.g., dozens or hundreds) rows of inputs + outputs + context, where each row was generated by an app usage.  Our insights are statistically derived from the distributions estimated by these rows.

[dbnl Eval](https://docs.dbnl.com/v0.20.x/using-distributional/python-sdk/eval-module) is our library that provides access to common, well-tested GenAI evaluation strategies.  You can use dbnl Eval to augment data in your app, such as the inputs and outputs.  Doing so produces a broader range of tests that can be run, and it allows dbnl to produce more powerful insights.