compliance-testing
EU AI Act compliance for high-risk AI systems is not a one-time checklist — it requires ongoing automated testing. This guide shows how to integrate bias, accuracy, robustness, audit trail, and logging tests into GitHub Actions using DeepEval and Giskard, with full YAML examples and a production monitoring setup using HelpMeTest
AI Testing
LLMs encode the biases present in their training data — and those biases surface in completions in ways that are measurable and testable. This guide covers what LLM bias looks like in practice, how to measure it with established benchmarks and DeepEval, how to build a bias test suite, and how
AI Testing
AI systems fail in ways traditional software doesn't — they drift, discriminate, and hallucinate, often silently. Risk-based testing for AI means probing bias across demographic groups, benchmarking accuracy under distribution shift, testing adversarial robustness, and measuring hallucination rates. This guide shows how to do all of it with code.
compliance-testing
The EU AI Act's full obligations for high-risk AI systems take effect in August 2026. If you're building AI-powered products in hiring, credit, healthcare, or similar domains, you need a concrete testing strategy right now — not a legal strategy, an engineering one. This guide covers what