AB Test Framework

AB Test Framework is a skill for comparing language models through structured A/B testing. It accepts two model identifiers and an array of test prompts, then runs side-by-side evaluations to determine which model performs better with a statistical confidence score.

Designed for teams evaluating model options or fine-tuning configurations, this framework provides a repeatable methodology for model selection decisions backed by data rather than intuition.

Key Features

• Structured model comparison accepting two model identifiers and an array of test prompts for systematic evaluation
• Confidence scoring that provides a statistical measure of how reliably one model outperforms the other
• Detailed results output including per-prompt comparison data, aggregate scores, and a winning model declaration
• Input validation with security measures including least privilege operations and audit logging
• Flexible test design allowing custom prompt arrays tailored to your specific use case requirements

Use Cases

• Compare two language models on your specific workload before committing to one for production use
• Evaluate fine-tuned model variants against baseline models with quantitative confidence metrics
• Run regression tests when upgrading model versions to verify performance is maintained
• Document model selection decisions with reproducible test results for team review

How It Works

The framework takes three parameters: model_a (first model identifier), model_b (second model identifier), and test_prompts (an array of test cases). It runs each prompt against both models, evaluates the responses, and produces a structured output with per-prompt results, an overall winner, and a confidence score indicating the reliability of the comparison.

Getting Started

Install the skill and prepare your test prompt array covering the scenarios most relevant to your use case. Provide the two model identifiers you want to compare and run the evaluation. Review the detailed results and confidence score to inform your model selection decision.