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Expression and readability metrics help you evaluate how well your AI communicates—not just what it says, but how it says it. These metrics are important when you want your AI to produce content that is clear, on-brand, and easy for users to understand. Use these metrics when you want to:
  • Ensure your AI’s responses match your brand’s voice and tone.
  • Check that generated content is clear, concise, and appropriate for your audience.
  • Quantitatively measure the quality of generated text compared to human-written references.
Below is a quick reference table of all expression and readability metrics:
NameDescriptionSupported NodesWhen to UseExample Use Case
ToneEvaluates the emotional tone and style of the response.Trace (root input/output only)When the style and tone of AI responses matter for your brand or user experience.A luxury brand’s customer service chatbot that must maintain a sophisticated, professional tone consistent with the brand image.
BLEU & ROUGEStandard NLP metrics for evaluating text generation quality.

These metrics are only available for experiments as they need ground truth set in your dataset.		LLM spanWhen you want to quantitatively assess the similarity between generated and reference texts.Evaluating the quality of machine-translated or summarization outputs against human-written references.

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