AI Function Generator

The AI Function Generator in CSVbox helps you create JavaScript functions and regex patterns without writing code manually. Describe what you want in plain English, and CSVbox generates compatible logic for your importer automatically. AI generation is available across:

Validation Functions
Virtual Columns
Data Transforms
Regex Column Type

Use it to build advanced import logic faster, reduce manual coding, and simplify complex validation or transformation workflows.

How it works

Open a supported editor

Open a supported editor in CSVbox.

Click Generate

Click the Generate button.

Describe the logic you want

Describe the logic in plain language. CSVbox generates compatible JavaScript or regex code.

Review the generated output

Review the output. All generated code remains fully editable before saving.

Insert and save the function

Insert the code and save the function.

Supported features

Feature	Generates
Validation Functions	JavaScript validation logic
Virtual Columns	JavaScript computed column logic
Data Transforms	JavaScript transformation logic
Regex Column Type	Regular expression patterns

Writing better prompts

The quality of the generated output depends heavily on how clearly you describe the requirement. For best results:

Mention exact column names when possible.
Clearly describe the expected output.
Include formatting requirements.
Add examples for complex logic.
Focus on one task at a time.
Avoid vague instructions.

AI generator for validation functions

Generate validation rules from plain-language descriptions instead of writing JavaScript validation logic by hand. Useful for:

Email validation
Phone number validation
Date validation
Required field checks
Cross-column validation
Business-specific validation rules

Example prompts

Email validation

Prompt

Validate that email addresses contain a valid @ symbol and domain

Expected behavior

"user@example.com" → valid
"user@" → invalid

Phone number validation

Prompt

Ensure phone numbers contain exactly 10 digits after removing special characters

Expected behavior

"(555) 123-4567" → valid
"123-456" → invalid

Date validation

Prompt

Check that dates are in YYYY-MM-DD format and are not future dates

Expected behavior

"2023-12-25" → valid
"12/25/2023" → invalid

Cross-column validation

Prompt

Ensure end_date is greater than start_date

Expected behavior

start_date="2023-01-01", end_date="2023-12-31" → valid
start_date="2023-12-31", end_date="2023-01-01" → invalid

Validation prompt tips

For better validation results:

Start with action words such as:
- validate
- ensure
- verify
- require
- check
Mention exact field names.
Describe the validation condition clearly.
Include valid and invalid examples for complex rules.
Mention how blank or null values should behave.

AI generator for virtual columns

Generate computed or derived columns from natural-language instructions. Instead of writing JavaScript logic by hand, describe what the virtual column should calculate or return. Useful for:

Combining fields
Calculated totals
Derived statuses
Formatting dates
Extracting values
Building custom labels

Example prompts

Full name column

Prompt

Combine first_name and last_name columns with proper capitalization

Expected behavior

"john" + "DOE" → "John Doe"

Total price calculation

Prompt

Multiply quantity and unit_price columns to create total_price

Expected behavior

quantity=5, unit_price=10.50 → 52.50

Date formatting

Prompt

Convert dates from MM/DD/YYYY to Month DD, YYYY format

Expected behavior

"12/25/2023" → "December 25, 2023"

Status generation

Prompt

Create a status column based on due_date

Expected behavior

Past date → "Overdue"
Within 7 days → "Due Soon"
Future date → "On Track"

Virtual column prompt tips

Mention which fields should be used.
Describe the expected output clearly.
Specify formatting rules.
Include examples for calculated logic.
Focus on one generated field at a time.

AI generator for data transforms

Generate data transformation logic from plain-language instructions to clean, normalize, or reformat imported data automatically. Useful for:

Text normalization
Formatting cleanup
Phone number cleanup
Date conversion
Value mapping
Row transformations

Example prompts

Email standardization

Prompt

Convert email addresses to lowercase and remove leading or trailing spaces

Expected behavior

" John.Doe@Example.COM " → "john.doe@example.com"

Phone number cleanup

Prompt

Remove all non-numeric characters from phone numbers

Expected behavior

"(555) 123-4567" → "5551234567"

Full name generation

Prompt

Combine first_name and last_name columns with proper capitalization

Expected behavior

"john" + "DOE" → "John Doe"

Date formatting

Prompt

Convert dates from MM/DD/YYYY format to YYYY-MM-DD

Expected behavior

"12/25/2023" → "2023-12-25"

Data transform prompt tips

Start with action words like:
- convert
- remove
- replace
- format
- normalize
- combine
Specify the target column.
Clearly describe the final output format.
Include sample input/output values for complex transforms.
Keep transformations focused and specific.

AI generator for regex patterns

Generate regular expression (regex) patterns from plain-language descriptions. Instead of writing regex syntax by hand, describe the format you want to validate and CSVbox generates the pattern automatically. Useful for:

Email validation
Phone number formats
ZIP/postal codes
Product code validation
Password rules
Custom formatting patterns

Example prompts

Phone number validation

Prompt

exactly 10 digits, no spaces or dashes

Generated pattern

/^[\d]{10}$/

Email validation

Prompt

valid email format with @ symbol and domain

Generated pattern

/^[^\s@]+@[^\s@]+\.[^\s@]+$/

ZIP code validation

Prompt

5 digit US zip code, optionally followed by hyphen and 4 digits

Generated pattern

/^\d{5}(-\d{4})?$/

Product code format

Prompt

product code starting with 2 uppercase letters followed by exactly 4 digits

Generated pattern

/^[A-Z]{2}\d{4}$/

Best practices

Keep prompts focused

Short, focused prompts produce better results than large multi-step instructions. Good:

Convert SKU values to uppercase

Less effective:

Convert SKU values to uppercase, remove spaces, validate duplicates, generate category names, and create status values

Use exact column names

Prefer:

Combine first_name and last_name

Instead of:

Combine the user's names

Mention formatting rules

Examples:

YYYY-MM-DD
10 digit phone number
uppercase product code
currency with 2 decimals

Include examples for complex logic

Adding examples improves accuracy significantly. For example:

Convert dates from MM/DD/YYYY to YYYY-MM-DD. Example: 12/25/2023 → 2023-12-25

Testing generated code

Always review and test generated code before saving. Recommended testing steps:

Review the generated output.
Test with sample values.
Test blank and null values.
Verify edge cases.
Confirm expected formatting.
Ensure only intended fields are modified.

Limitations

Current limitations

Generates JavaScript functions and regex patterns only.
External APIs and network requests are not supported.
Browser APIs like localStorage and cookies can’t be used.
Complex multi-step workflows may require multiple functions.

Unsupported operations

The AI can’t:

Add or remove rows
Modify your importer schema
Access external systems
Call third-party APIs
Access databases
Read files outside CSVbox
Perform actions outside the CSVbox execution environment

Common issues

Generation failed

Possible causes

Network issue
Temporary server-side issue
Prompt too complex
Prompt too ambiguous

Solutions

Retry after a few seconds.
Simplify the prompt.
Make instructions more specific.
Break large logic into smaller prompts.

Generated output is incorrect

Possible causes

Missing formatting details
Ambiguous instructions
Missing edge cases
Unclear expected output

Solutions

Add input/output examples.
Mention formatting rules explicitly.
Specify null or blank value handling.
Focus on one task at a time.

Tips for better results

Use exact field names whenever possible.
Keep prompts concise and specific.
Mention edge cases such as blank values.
Include examples for complex rules.
Generate one function at a time.
Review generated code before saving.
Test thoroughly before production use.

Security and safety

Generated code runs within the CSVbox execution environment and follows CSVbox security restrictions. Generated functions can’t:

Access external systems
Make network requests
Access browser storage
Modify importer configuration automatically

Review and test all generated code before production use.

​How it works

​Supported features

​Writing better prompts

​AI generator for validation functions

​Example prompts

​Email validation

​Phone number validation

​Date validation

​Cross-column validation

​Validation prompt tips

​AI generator for virtual columns

​Example prompts

​Full name column

​Total price calculation

​Date formatting

​Status generation

​Virtual column prompt tips

​AI generator for data transforms

​Example prompts

​Email standardization

​Phone number cleanup

​Full name generation

​Date formatting

​Data transform prompt tips

​AI generator for regex patterns

​Example prompts

​Phone number validation

​Email validation

​ZIP code validation

​Product code format

​Best practices

​Keep prompts focused

​Use exact column names

​Mention formatting rules

​Include examples for complex logic

​Testing generated code

​Limitations

​Current limitations

​Unsupported operations

​Common issues

​Generation failed

​Possible causes

​Solutions

​Generated output is incorrect

​Possible causes

​Solutions

​Tips for better results

​Security and safety

How it works

Supported features

Writing better prompts

AI generator for validation functions

Example prompts

Email validation

Phone number validation

Date validation

Cross-column validation

Validation prompt tips

AI generator for virtual columns

Example prompts

Full name column

Total price calculation

Date formatting

Status generation

Virtual column prompt tips

AI generator for data transforms

Example prompts

Email standardization

Phone number cleanup

Full name generation

Date formatting

Data transform prompt tips

AI generator for regex patterns

Example prompts

Phone number validation

Email validation

ZIP code validation

Product code format

Best practices

Keep prompts focused

Use exact column names

Mention formatting rules

Include examples for complex logic

Testing generated code

Limitations

Current limitations

Unsupported operations

Common issues

Generation failed

Possible causes

Solutions

Generated output is incorrect

Possible causes

Solutions

Tips for better results

Security and safety