This article provides a concise reference for evaluating multi-step AI agents and agentic systems. It covers core metrics for task completion, reasoning, and efficiency, and highlights recent advances in safety, alignment, semantic evaluation, plan consistency, and online monitoring. A practical bullet-point workflow is included for both research and production contexts.
Modern AI systems may look diverse on the surface, but under the hood they rely on a small set of recurring architectural and training ideas. This article distills foundational concepts—ranging from tokenization and decoding to RAG, diffusion models, and LoRA—that every ML engineer should understand to design, debug, and reason about real-world AI systems.
Thinking and describing how Spotify's Discover Weekly leverages machine learning and statistical models to generate personalized music recommendations.
This article uses a fruit basket analogy to explain key concepts in machine learning evaluation metrics, including recall, precision, and F1 score, making it easier to understand how these metrics relate to identifying true positives, false positives, and false negatives.