This article provides a concise reference for evaluating large language models (LLMs) and retrieval-augmented generation (RAG) systems. It covers core metrics like accuracy, F1, BLEU, ROUGE, and perplexity, and highlights recent advancements in safety, alignment, semantic evaluation, hallucination detection, operational metrics, and multilingual performance. A practical bullet-point workflow is included for both research and production settings.
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.
Modern data and AI products are built on a small set of recurring Python tools for data processing, visualization, interfaces, and APIs. This article provides a concise conceptual overview of Pandas, Polars, visualization libraries, dashboard frameworks, and backend web frameworks—highlighting how they fit together in real-world systems.
Thinking and describing how Spotify's Discover Weekly leverages machine learning and statistical models to generate personalized music recommendations.