prompt-evaluator

To evaluate an LLM prompt, use a multi-dimension rubric that scores prompt quality across Clarity, Specificity, Completeness, Conciseness, Structure, Grounding, Safety, and Robustness. This generates a numeric score with actionable rewrite suggestions.

Yes, you can compare two LLM prompts using A/B comparison mode. This evaluates both prompts against the same rubric dimensions, producing scored metrics to determine which prompt yields better clarity, specificity, and overall effectiveness.

The best way to optimize prompts is to apply a systematic evaluation that produces actionable rewrite suggestions. This analyzes your instructional text for completeness and robustness, generating an optimized rewrite tailored for large language models.

You can batch evaluate multiple system prompts using batch evaluation workflows. This processes several instructional texts simultaneously, applying the 8-dimension rubric to generate quantitative scores and rewrite suggestions for each prompt.

Prompt evaluation works for system prompts, user prompts, and any instructional text destined for an LLM. It systematically assesses the prompt's grounding and safety dimensions to ensure the input is effective across different LLM interaction types.