Evals

Complete Guide to RAG Evaluation: Metrics, Methods, and Best Practices for 2025

Complete Guide to RAG Evaluation: Metrics, Methods, and Best Practices for 2025

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
Kuldeep Paul
Evaluating Agentic AI Systems: Frameworks, Metrics, and Best Practices

Evaluating Agentic AI Systems: Frameworks, Metrics, and Best Practices

TL;DR Agentic AI systems require evaluation beyond single-shot benchmarks. Use a three-layer framework: System Efficiency (latency, tokens, tool calls), Session-Level Outcomes (task success, trajectory quality), and Node-Level Precision (tool selection, step utility). Combine automated evaluators like LLM-as-a-Judge with human review. Operationalize evaluation from offline simulation to online production monitoring
Navya Yadav
Building a Robust Evaluation Framework for LLMs and AI Agents

Building a Robust Evaluation Framework for LLMs and AI Agents

TL;DR Production-ready LLM applications require comprehensive evaluation frameworks combining automated assessments, human feedback, and continuous monitoring. Key components include clear evaluation objectives, appropriate metrics across performance and safety dimensions, multi-stage testing pipelines, and robust data management. This structured approach enables teams to identify issues early, optimize agent behavior systematically,
Kamya Shah