Kuldeep Paul

Retrieval-Augmented Generation (RAG) systems have become foundational architecture for enterprise AI applications, enabling large language models to access external knowledge sources and provide grounded, context-aware responses. However, evaluating RAG performance presents unique challenges that differ significantly from traditional language model evaluation. Research from Stanford's AI Lab indicates that

TL;DR: Enterprise AI evaluation must cover three layers end to end: experiment, evaluate, and observe. Choose a platform that unifies offline evals, agent simulations, and online evals in production, and integrates with your observability stack. Priorities for 2025 include OpenTelemetry compatibility, human-in-the-loop pipelines, dataset curation from production logs, and

As AI agents are becoming more mainstream and have started powering mission-critical business workflows, evaluating your AI Agents has become imperative. In 2025, the landscape of AI evaluation tools has matured rapidly, offering teams not just basic benchmarking but comprehensive observability, simulation, and evaluations to test their AI applications end-to-end.

TL;DR Advanced AI models currently solve less than 2% of problems in FrontierMath, a benchmark designed by expert mathematicians to test research-level mathematical reasoning. This represents a significant gap between current AI capabilities and human-level mathematical expertise. As AI systems approach this milestone, organizations must prepare with robust evaluation