How to Test Generative AI Applications: Methods, Tools, and Challenges
Testing generative AI means covering risks static testing doesn't touch: hallucination, non-determinism, unsafe output. Methods, tools and challenges.
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AI 77
Geology 27
Benchmarking 25
Machine Learning 18
Evaluation 12
Agentic AI 11
Open Source 9
Reinforcement Learning 5
China 4
AI Adoption 3
Evaluation
Model Accuracy: What It Measures, Its Limits, and How to Improve It
Model accuracy is the most-cited performance number and the most misread. When it is the right measure, when it isn't and how to improve it.
Evaluation Metrics in Machine Learning: Key Metrics and How to Choose
Evaluation metrics quantify how well a model performs, but the right metric depends on the task. Key machine learning metrics and how to choose.
Aspect-Based Sentiment Analysis (ABSA): Types, Examples, and How to Train a Model
Aspect-based sentiment analysis goes beyond positive or negative: extract granular sentiment by target, with architectures, methods and applications.
Weak Supervised Learning: Types, Techniques, and Frameworks
Weak supervised learning trains models on noisy, imprecise or indirect labels, not hand-labeled ground truth. Types, techniques and frameworks.
Synthetic Data vs Real Data: Differences, Limitations, and When to Use Each
Synthetic and real data fail differently and suit different stages of an AI pipeline. Differences, limitations and when combining them works best.
What Is Model Drift in AI? Types, Causes, and How to Detect It
Model drift is when a model's performance degrades after deployment as the world it learned from changes. Types, causes and how to detect it.
LLM Benchmark Leaderboards: How to Compare Models Fairly
A guide to reading LLM benchmark leaderboards: methodology, what's held constant, and how to compare models without being misled by rankings alone.
AI Evaluation Framework: How to Build a Reliable Evaluation System
Learn what an AI evaluation framework includes (dataset, rubric, judge, metrics, reporting), how agents change it, build vs buy, and common failure modes.
Ground Truth Data for AI: What It Is and How to Build It Reliably
Learn what ground truth data for AI is, how it is built, why its quality decides evaluation results, and the errors that quietly corrupt scores.