Kuldeep Paul

TL;DR Choosing the right LLM evaluation platform is critical for shipping reliable AI agents in 2026. This comprehensive comparison examines the top 5 platforms: Maxim AI leads with end-to-end simulation, evaluation, and observability; Braintrust offers strong experiment tracking; LangSmith provides deep LangChain integration; Arize excels in ML monitoring; and

TL;DR: Evaluating RAG systems requires specialized platforms that measure both retrieval quality and generation accuracy. This guide covers the five leading RAG evaluation platforms in 2026: Maxim AI (comprehensive platform with simulation, evaluation, and observability), LangSmith (LangChain-focused tracing), Arize Phoenix (open-source observability), Ragas (reference-free evaluation framework), and DeepEval (pytest-style

Pre-built evaluation metrics cover common quality dimensions like accuracy, relevance, and coherence. However, production AI applications require validation against domain-specific business rules, compliance requirements, and proprietary quality standards that generic evaluators cannot assess. Custom evaluators enable teams to enforce these specialized quality checks across AI agent workflows, ensuring applications meet