# Constraints

Do you have **business rules** in your dataset? These are deterministic rules that every single row in your data *must* follow in order to be considered valid. By default, SDV synthesizers are *probabilistic* so they may not learn to match your rule 100% of the time.

The good news is that you can input your business rules into your synthesizer using **constraints**. Our constraint-augmented generation ensures that your synthetic data meets the constraint — 100% of the time.

## Constraint Example

One example of a business rule is when the values in one column always have to be greater than values in another column. This is true for every single row of data.

<figure><img src="/files/LdCvhcE1qaSKj8tROt47" alt=""><figcaption><p>In this business rule, the <code>checkout_date</code> must be greater than the <code>checkin_date</code> for all rows.</p></figcaption></figure>

You can supply this business rule to a synthesizer using using an [Inequality constraint](/sdv/concepts/constraint-augmented-generation-cag/predefined-constraints/inequality.md).

```python
from sdv.cag import Inequality

my_constraint = Inequality(
    low_column_name='checkin_date',
    high_column_name='checkout_date'
)

my_synthesizer.add_constraints(constraints=[
    my_constraint
])
```

## Resources

Please refer to our [**Constraint-Augmented Generation**](/sdv/concepts/constraint-augmented-generation-cag.md) guide for the API reference, a list of predefined constraints, and instructions for programming your own constraint.


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