Remove unnecessary characters, normalize case and abbreviations, and filter clutter words.

Clean and standardize the following Job Title.
The cleaning process should include the following steps:
- Remove noise. Remove any special characters (emojis, punctiation) that do not contribute to the job title meaning.
- Normalize Capitalization: Ensure consistent capitalization (e.g., capitalize the first letter of each word unless it's a common preposition or conjunction).
- Consolidate Variations: Merge variations of the same job title (e.g., 'Software Engineer', 'Software Developer', and 'Programmer' should all be standardized to 'Software Engineer').
- Handle Abbreviations: Expand or standardize common abbreviations (e.g., 'Sr.' to 'Senior').
- Filter Out Non-Job Titles: Remove entries that do not represent actual job titles (e.g., phrases like 'Seeking Opportunities', 'Open to Work').

The job title to clean: %job title%

How to use this prompt on a CSV file?

Datablist allows you to take an entire CSV file and run prompts directly on every line. Whether you're cleaning data, generating summaries, or transforming text, this method saves hours.

Open Datablist (it's free)

The great news is that the prompt you need is already available in Datablist under the "Templates" section. No need to start from scratch! Simply select the template that fits your task, and it’s ready to run on every line of your CSV. This makes it even easier to transform your data with just a few clicks.

Import your CSV file in Datablist. Then select "Enrich" from the header.

Select Enrich
Select Enrich

Then select the template: Clean and standardize job title

Select Enrich
Select Template

Replace the variables from the prompt with your collection's properties using {{

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Define variables using your collection's properties