LLM and RAG Evaluation: Metrics, Best Practices

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...

Core Concepts Behind Modern AI Systems

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...

Core Tools in the Modern Python Data Analytics Stack

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...

Understanding ML Metrics: Recall, Precision, and F1 Score

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...

The Current Landscape and Emerging Trends in AI Agents