← 📊 Evaluation & ML FoundationsNEXT IN EVALUATION & ML FOUNDATIONSContrastive and Metric Learning→
Core
Benchmarks and Their Limits
Public benchmarks like MMLU give a shared yardstick, but they saturate, leak into training corpora, and stop tracking real ability once labs optimize for them. Contamination (test items in the training data) and Goodhart's law (a measure that becomes a target stops measuring) are why a high leaderboard score can be meaningless on your workload. Applied AI interviews probe this to see whether you trust a number or build a private eval set on your own distribution.
a free account unlocks the core curriculum tier · no card
RELATED CONCEPTS
PRACTICE THIS IN REAL QUESTIONS
LLM & GenAI FundamentalsWhat are MMLU, HumanEval, and GSM8K, and how do you interpret LLM benchmark scores?→Machine Learning & Data ScienceYour churn model's AUC jumps from 0.71 to 0.93 after adding a 7-day rolling feature. What now?→LLM & GenAI FundamentalsHow do you detect and prevent benchmark contamination, and why are public LLM leaderboards often inflated?→RAG & Agent System DesignDesign a production RAG system over 10M documents serving ~1,000 QPS at sub-second latency.→AI Security, Privacy & GovernanceA tool-using agent reads untrusted web content. How do you defend against prompt injection?→RAG & Agent System DesignWhen do you build an agent instead of a single LLM call, and how do you keep a multi-step agent reliable?→
COMPANIES THAT ASSUME THIS