Evals

TL;DR Building robust evaluation datasets is critical for measuring and improving AI agent performance. This guide covers the complete lifecycle of dataset creation—from defining objectives and sourcing data to curating multi-modal examples and implementing continuous improvement workflows. Quality evaluation datasets enable teams to run meaningful evaluations, catch regressions

TL;DR Testing and evaluating AI agents in production requires a systematic approach that combines pre-deployment validation, continuous monitoring, and iterative improvement. This guide covers essential strategies including simulation-based testing, multi-layered evaluation frameworks, real-time observability practices, and data curation techniques. Organizations can achieve reliable AI agent deployments by implementing comprehensive

TL;DR A/B testing is essential for optimizing AI agent prompts and improving application performance. This systematic approach allows teams to compare prompt variations, measure real-world impact, and make data-driven decisions. Research shows that prompt design significantly affects output quality, with variations producing performance differences of up to 40%

TL;DR Simulating and evaluating AI agents requires systematic testing across diverse scenarios, multi-dimensional metrics, and robust frameworks that combine automated evaluation with human oversight. Organizations must implement simulation environments to test agent behavior before deployment, establish clear success criteria across accuracy, efficiency, and safety dimensions, and integrate continuous monitoring

TL;DR Evaluating AI agents requires a systematic approach that goes beyond traditional software testing. Organizations deploying autonomous AI systems must implement evaluation-driven development practices, establish multi-dimensional metrics across accuracy, efficiency, and safety, create robust testing datasets with edge cases, balance automated evaluation with human oversight, and integrate continuous monitoring

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