Testing generative AI applications means covering risks static software testing doesn't touch: hallucination, non-determinism, unsafe output. The methods, tools and challenges involved.
Agentic AI benchmarks test whether an AI agent can finish real tasks with tools. What SWE-bench, τ-bench, GAIA and others check, and what they miss.
Synthetic and real data fail differently and suit different stages of an AI pipeline. The core differences, the limitations, and when combining them beats choosing one.
Model drift is what happens when a model's performance degrades after deployment as the world it was trained on changes. The types, the causes, and how teams detect it.
A guide to reading LLM benchmark leaderboards: methodology, what's held constant, and how to compare models without being misled by rankings alone.
What an AI evaluation framework actually includes (datasets, rubrics, judges, metrics and reporting) and how the pieces fit together into one reliable system.
In a source-grounded benchmark, extracted text is the evidence. How pdftotext modes, OCR damage and pseudonymisation quietly break evidence anchors.
What ground truth data is, how it's created and validated, and why extraction and labeling choices quietly become benchmark choices.
AI model evaluation explained: four methods, how to choose metrics, and how to read results, with numbers from grading eight models on 150 questions.
Benchmark statistical significance in practice: confidence intervals, minimum detectable gaps, and a fairness check on a 50-question AI benchmark.