# Prompt A/B Test Tool: Score Two Variants

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# Prompt A/B Test Tool

 

 
 By Ivo Donker — compiled with AI assistance (Claude & Gemini)

 
 Purpose of this tool: Compare two prompt variants and their corresponding LLM outputs directly. Enter the texts, set the weight and score (1-5) for each criterion, and read the weighted final result right away.
 

 
 
 
 Enable blind mode (hides 'A' and 'B' to prevent anchoring bias)
 
 
 Clear assessment
 Copy result
 
 

 
 
 
 
### Variant A

 
 Prompt A
 
 
 
 Model output A
 
 
 
 Output Statistics (Estimate):
 
 
- Characters: 0
 
- Words: 0
 
- Tokens (rough estimate): 0
 
 
 

 
 
 
### Variant B

 
 Prompt B
 
 
 
 Model output B
 
 
 
 Output Statistics (Estimate):
 
 
- Characters: 0
 
- Words: 0
 
- Tokens (rough estimate): 0
 
 
 
 

 
## Evaluation Criteria

 

 
 
### Weighted Final Result

 
 
 Score Variant A
 0.00 / 5
 
 
 Difference (Absolute Delta)
 0.00
 
 
 Score Variant B
 0.00 / 5
 
 
 Enter assessments to see a result.
 

 
 
## How to Use This Tool Wisely

 
 Systematically comparing prompt variants is essential for building reliable LLM applications. To avoid making decisions based on chance or personal preferences, a structured approach is necessary. Learn more about the fundamentals in our guide on [A/B testing prompts](/en/ab-testen-prompts).
 

 
 Keep the following guidelines in mind while evaluating:
 

 
 
- Isolate variables: Change only one part of the prompt per test (for example, adding an explicit format or a role instruction). Do not change the temperature or model type between two runs.
 
- Limit anchoring bias with blind scoring: Use the built-in 'Blind mode'. If you know which prompt is yours or which method you prefer, you will unconsciously judge the output with bias.
 
- Define criteria in advance: Determine what is important before you read the results. Is factual accuracy decisive, or is it really about writing style? Assign the appropriate weights using an established [self-evaluation framework](/en/zelf-evalueren-raamwerk).
 
- Run multiple runs: LLMs are stochastic. A single generation can be lucky or unlucky. For proper quality assurance, it is wise to also combine [human evaluation](/en/menselijke-evaluatie) with larger test sets.
 
 
 Want to discuss your test results or evaluation methodologies? Join the conversation in our [LLMnet Community](https://community.llmnet.nl/en/).
 

 
 

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