Smart Columns

If you want to get truly valuable insights from your data, step one is making sure it’s clean, structured, and enriched with the right information. That’s exactly why we built Smart Columns.

With smart columns, you can:

• Recode values into cleaner categories

• Apply formulas to transform data

• Extract new attributes like emotions or brand mentions

There are three modes you can choose from:

 Mappings, Formulas, and LLMs. Let’s go through them step by step

 

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Quick Start 

Regardless of the mode you choose, creating your first Smart Column is easy:

1. Pick your source column → the data you want to transform (e.g. Language, Text to analyze, or Country).

2. Choose a mode → Mapping, Formula, or LLM, depending on your goal.

3. Configure the transformation → add logic, mappings, prompts, or formulas.

 

How to Select Your Source Column

Before anything can be transformed, you need to tell Caplena which column to use.

Here's how:

1. Click into the Expression for "Source Value" field.

2. Click the {ƒx} icon to the right — this opens the variable selector.

3. Choose from available project variables (like Text to analyze, Country, etc.).

4. Caplena will insert the correct variable syntax automatically:

    

    

    

1. Mapping Mode 

Got inconsistent values that need standardizing? Mapping mode is your best friend. You define a source value and map it to a desired output.

Example:

• EN → English

• IT → Other

• LV → Latvian

• ❓ Anything else → becomes your fallback value (e.g., “Other”)

You can enter mappings manually or upload them as a JSON dictionary, perfect for large sets.

 Once set up, this column auto-updates whenever you upload new data.

 2. Formula Mode 

Want to reverse a string, calculate something, or clean up a date? Use Formula mode, powered by Jinja syntax.

Example: Reverse a String

To reverse the content of a column (e.g., Text to analyze), use:

This formula:

• Converts the string into a list of characters

• Reverses that list

• Joins it back into a string

 Output: Caplena is awesome → emosewa si anelpaC

Formulas are incredibly flexible for working with numbers, text, dates, or any custom logic you need.

 

 

This mode applies AI models (LLMs) to process free-text inputs, enabling more advanced use cases like summarization or classification.

Common use cases:

• Extracting brand names

• Detecting emotion or sentiment

• Removing slurs or sensitive content

• Creating summaries

Templates Available

Caplena provides a growing template library with ready-to-use prompts for common use cases. These templates help you:

• Get started quickly

• Ensure consistent and high-quality results

• Customize further if needed

You can start from a template and then edit the prompt to match your specific use case.

Best Practices

• Start small: Test your logic on a small dataset or with a few rows using the preview.

• Use templates: Especially for LLM tasks, templates can save time and provide a solid base.

• Use fallback values: Always define a fallback in mappings to handle unexpected inputs.

• Name your columns clearly: This helps when chaining multiple Smart Columns together.

• Chain columns: You can use the output of one Smart Column as the input for another (e.g., extract brand → map to numeric code).

• Keep prompts concise: For LLMs, simpler prompts tend to yield more consistent results.