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When analyzing open-text feedback, you may want to isolate comments related to a specific topic, such as pricing, onboarding, churn risk, usability, or billing. While automatic topic assignment helps explore themes, some use cases require more control over how a topic is defined or extracted. An LLM-powered Smart Column allows you to apply custom logic to either:
  • Classify comments (e.g., Yes/No, labels)
  • Extract specific comments related to a topic
  • Return structured outputs based on defined criteria

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Practical Use Cases

You might use a Smart Column when you want to:
  • Detect churn risk only when cancellation intent is clearly expressed
  • Flag pricing complaints only when dissatisfaction is mentioned
  • Extract comments describing implementation challenges
  • Identify feedback about a specific process stage (e.g., onboarding phase)
  • Separate product issues from service-related issues
These scenarios often require stricter logic than topic clustering provides.

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Best Practices

1

Step 1: Define the Topic Clearly

Avoid vague instructions such as:
“Extract comments about pricing.”
Instead, define the scope:
Identify comments that refer to pricing structure, subscription costs, perceived value for money, or price transparency.
2

Step 2: Add Inclusion and Exclusion Criteria

Example structured prompt:
Determine whether the comment refers to [TOPIC].

Include comments that clearly describe or evaluate defined elements of [TOPIC].

Exclude comments that mention the topic only incidentally or focus primarily on other themes.
You can then configure the output as:
  • Yes/No classification
  • Summaries
  • Extracted comments
  • Structured categories
Last modified on May 23, 2026