Akab
Provides a production-grade A/B testing framework with three levels of scientific rigor for comparing large language model (LLM) outputs.
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Akab is a comprehensive A/B testing framework designed specifically for evaluating LLM outputs with varying degrees of scientific rigor. It offers a unified approach, from quick, unblinded explorations for debugging and rapid iteration, to fully blinded scientific experiments requiring statistical significance. The framework ensures production-grade implementation with real API calls and results, strong scientific methodologies including statistical analysis and reproducibility, and dynamic success criteria for highly customizable tests. It also incorporates intelligent assistance to streamline the testing process, making it the definitive component for robust model comparisons within the Atlas system.
Características Principales
- Production-Grade Implementation with Real API Calls and Results
- Three-Level Testing Architecture (Quick Compare, Campaign, Experiment)
- Scientific Rigor with Statistical Analysis and Blinding Options
- Intelligent Assistance for Constraint Suggestions and Error Recovery
- 0 GitHub stars
- Dynamic Success Criteria using Configurable Metrics and Constraints
Casos de Uso
- Debugging and rapid iteration of LLM prompts and model behaviors
- Conducting production A/B tests and performance comparisons for LLMs
- Performing unbiased scientific evaluation and academic research on LLM capabilities
