Golden Sets: A Testing Framework for Evaluating Probabilistic AI Systems
Golden sets are unit tests for probabilistic behavior: curated cases, versioned rubrics, and gates that prevent quality regressions from shipping as surprises.
Read the full articleYou might also wanna read
Dual-Layer Testing Framework for AI-Infused Applications: Combining Deterministic and Probabilistic Quality Assurance
Reliable AI delivery requires conventional testing for functionality and probabilistic evaluation for quality, safety, and deployment confid

The Economics of AI-Driven Testing
The Economics of AI-Driven Testing
AI Evaluation: Breaking the i.i.d. Myth
AI's reliance on random dataset splits for performance evaluation falters in fields like aerial surveillance and agriculture. A new framewor
Probabilistic Design: Embracing Uncertainty in AI-Driven UX Decision-Making
In a world where AI is informing more design choices, it’s easy to mistake predictions for certainties. This article introduces Probabilisti

Project Kaleidoscope: Contextual, Human-Aligned Evaluation for Real-World AI Applications
arXiv:2607.14673v1 Announce Type: new Abstract: Evaluations (Evals) are a deployment bottleneck for real-world AI applications: public bench
How to Self-Test a Low-Cost AI Coding Route Before Trusting It With Real Work
A developer has outlined a practical self-testing framework for evaluating whether a cheaper AI model, such as GLM-5.2, can reliably substit

Comments
Sign in to join the conversation.
No comments yet. Be the first.