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docs: mention custom columns
Signed-off-by: Johnny Greco <jogreco@nvidia.com>
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docs/devnotes/posts/have-it-your-way.md

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@@ -75,6 +75,8 @@ The first plugin boundaries match the places where real projects most often need
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These boundaries are intentionally narrow. A plugin should own the behavior that is specific to your use case. Data Designer should keep owning the pipeline responsibilities: validation, dependency resolution, batching, model calls, logging, previews, output handling. That split lets custom components use the normal workflow without moving orchestration into the project.
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What about [custom columns](../../concepts/custom_columns.md)? Start with a custom column when you are prototyping column-generator behavior or need a one-off column that only one project uses. Custom columns keep the logic in a Python function inside the config, with declared dependencies and optional model access. When that logic needs a stable config schema, tests, packaging, docs, or reuse across teams, promote it to a column generator plugin.
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## **Author a Plugin: From Glue Code to Seed Reader**

fern/versions/v0.5.8/pages/devnotes/posts/have-it-your-way.mdx

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These boundaries are intentionally narrow. A plugin should own the behavior that is specific to your use case. Data Designer should keep owning the pipeline responsibilities: validation, dependency resolution, batching, model calls, logging, previews, output handling. That split lets custom components use the normal workflow without moving orchestration into the project.
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What about [custom columns](/concepts/custom-columns)? Start with a custom column when you are prototyping column-generator behavior or need a one-off column that only one project uses. Custom columns keep the logic in a Python function inside the config, with declared dependencies and optional model access. When that logic needs a stable config schema, tests, packaging, docs, or reuse across teams, promote it to a column generator plugin.
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## Author a Plugin: From Glue Code to Seed Reader

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