> ## Documentation Index
> Fetch the complete documentation index at: https://futureagi.mintlify.site/llms.txt
> Use this file to discover all available pages before exploring further.

# Create Dynamic Column by Conditional Node

> A **conditional node** is a dynamic column type that applies **branching logic** (if/elif/else) to determine operations on each row of a dataset.

### **1. Accessing the Column Creation Interface**

To create a conditional node column, go to the **Data** tab in your dataset and click the **+ Add Columns** button. In the **Dynamic Columns** section, select **Conditional Node**.

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### **2. Configuring the Conditional Node**

Once selected, configure the following settings:

* **Name** – Assign a name to this new column.
* Each row in the dataset is processed based on the **branching logic** defined in the conditional node:
  * **If Condition** – The first condition to check.
  * **Elif Conditions (optional)** – Additional conditions checked sequentially if the first condition is false.
  * **Else Condition** (optional) – The default fallback when none of the conditions match.
* **Choosing an Operation Type:** The system allows various operations when conditions are met
  * **[Run Prompt](/future-agi/get-started/dataset/create-dynamic-column/using-run-prompt)** – Generates AI-driven responses using custom LLM prompts.
  * **[Retrieval](/future-agi/get-started/dataset/create-dynamic-column/using-vector-db)** – Fetches relevant data from a vector database via similarity search.
  * **[Extract Entities](/future-agi/get-started/dataset/create-dynamic-column/by-extracting-entities)** – Identifies and extracts key information from text columns.
  * **[Extract JSON Key](/future-agi/get-started/dataset/create-dynamic-column/by-extracting-json)** – Retrieves specific values from JSON-formatted dataset columns.
  * **[Execute Custom Code](/future-agi/get-started/dataset/create-dynamic-column/by-executing-code)** – Runs Python scripts for custom row-level transformations.
  * **[Classification](/future-agi/get-started/dataset/create-dynamic-column/using-classification)** – Assigns labels to dataset rows using a pre-trained AI model.
  * **[API Calls](/future-agi/get-started/dataset/create-dynamic-column/using-api-calls)** – Integrates external APIs to fetch and populate dynamic column data.

Once created, the system evaluates each row, applying the conditional logic in sequence:

1. **Evaluates Conditions** – Checks `if`, `elif`, and `else` in order.
2. **Executes Matching Operation** – Applies the corresponding transformation.
3. **Stores Results** – Saves the generated values in the new column.

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### **Best Practices for Conditional Nodes**

* Ensure **clear condition hierarchy** (if → elif → else) to prevent logical conflicts.
* Match **data type** with the intended operation to avoid conversion issues.
* Use **text transformation** for modifying string data dynamically.
* Apply **classification logic** for structured labelling of dataset rows.
* If integrating **API calls**, ensure external sources return expected results.

Conditional nodes enable flexible and automated data transformations, allowing datasets to adapt dynamically based on logic-driven workflows.

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