LLM Evaluation

A comprehensive guide and checklist for evaluating AI agents, covering error analysis, dataset construction, grader design, and production readiness.

A comprehensive guide on creating high-fidelity evaluation systems for AI agents, focusing on data sourcing, metric definition, and iterative experimentation.

A deep dive into the methodology of evaluating 'skills' for coding agents like Claude Code and DeepSeek, focusing on LangChain integration and LangSmith evaluation frameworks.

Discover how NVIDIA NeMo Evaluator Agent Skills streamline the evaluation of conversational LLMs using NVIDIA NIM and automated judge models to reduce latency and cost.

Building reliable AI agents requires more than just a prompt; it demands a deep understanding of reasoning traces and a rigorous evaluation framework powered by observability.

Explore how monday Service implemented a robust, code-first evaluation framework using LangSmith to ensure reliability and performance in their customer service AI agents.

A comprehensive guide to evaluating Retrieval-Augmented Generation (RAG) systems, covering retrieval metrics, generation faithfulness, and automated evaluation frameworks using n1n.ai.

OpenAI is reportedly asking contractors to upload past work artifacts to train its upcoming autonomous AI agents, shifting the burden of data anonymization to the workers themselves.

As the industry anticipates Claude Opus 4.5, evaluating Large Language Models is becoming harder than ever due to data contamination and the 'jagged frontier' of AI capabilities.