AI CSV import cleanup workflow checklist

Checklist

Raw source material is preserved before AI edits begin.The desired output format is written explicitly.Edge cases, missing values, and exceptions are listed.The first output is reviewed against real examples.The final prompt is stored for reuse.Sensitive or policy-heavy cases get manual review.Document source and target systems.List required fields and accepted formats.Ask AI for mapping and validation rules.Compare row counts before and after cleanup.

Mistakes to avoid

skipping row-count validation

Make the prompt more specific, keep the source data visible, and review the output before using it in a live workflow.

editing the only file copy

Make the prompt more specific, keep the source data visible, and review the output before using it in a live workflow.

assuming delimiter and encoding are correct

Make the prompt more specific, keep the source data visible, and review the output before using it in a live workflow.

not checking required field formats

Make the prompt more specific, keep the source data visible, and review the output before using it in a live workflow.

Related workflow pages

Use the AI CSV import cleanup workflow prompt builderPrepare messy CSV files for imports by validating fields, row counts, delimiters, and required formats.Copy AI CSV import cleanup workflow prompt templatesPrepare messy CSV files for imports by validating fields, row counts, delimiters, and required formats.Compare AI tools for AI CSV import cleanup workflowPrepare messy CSV files for imports by validating fields, row counts, delimiters, and required formats.Follow the AI CSV import cleanup workflow workflowPrepare messy CSV files for imports by validating fields, row counts, delimiters, and required formats.AI CSV import cleanup workflowPrepare messy CSV files for imports by validating fields, row counts, delimiters, and required formats.Use the CSV validation checklist with AI prompt builderBuild AI-assisted validation checks for row counts, required fields, date formats, duplicates, and bad values.