AI Evaluation Lab
ZenXData AI Evaluation Lab
Research, guides and field notes from the team that benchmarks, evaluates and stress tests AI systems for a living. The Lab shares what we learn from real evaluation programs — model benchmarking, human evaluation, red teaming, speech AI and more.
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LLM evaluation
Why automated metrics are not enough to evaluate LLMs
Benchmarks and LLM-as-judge scores are useful, but they routinely miss the failures users notice first. Here is how to combine them with structured human evaluation.
AI model benchmarking
Designing a custom benchmark for your AI product
Public leaderboards rarely reflect your use case. A practical guide to building a benchmark that predicts real-world performance.
AI red teaming
Red teaming AI agents: what breaks when models can act
Tool use, browsing and multi-step planning introduce new failure surfaces. What we look for when testing agents.
Speech AI
Evaluating speech AI across accents, dialects and environments
ASR and TTS quality varies dramatically across speaker populations. How to build coverage that reflects your real users.
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