Evals

Mastering RAG Evaluation Using Maxim AI

Mastering RAG Evaluation Using Maxim AI

If your customers depend on your AI to be right, your retrieval augmented generation pipeline is either earning trust or eroding it on every query. Maxim AI helps teams continuously measure retrieval quality, groundedness, and user outcomes across the RAG lifecycle. The difference often comes down to what you measure

LLM as a Judge

LLM as a Judge: A Practical, Reliable Path to Evaluating AI Systems at Scale

AI evaluation has shifted from static correctness checks to dynamic, context-aware judgment. As applications evolve beyond single-turn prompts into complex agents, tool use, and multi-step workflows, teams need evaluation that mirrors how users actually experience AI. Enter “LLM as a Judge” — using a model to evaluate other

Top 5 AI Evals Tools for Enterprises in 2025

Top 5 AI Evals Tools for Enterprises in 2025: Features, Strengths, and Use Cases

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

Session-Level vs Node-Level Metrics: What Each Reveals About Agent Quality

Session-Level vs Node-Level Metrics: What Each Reveals About Agent Quality

AI quality comes down to two things: how well the agent performs across a whole session, and how solid each step inside that session is. Session metrics tell you if the agent hit the goal and kept it safe for the user. Node metrics dig into every action and decision

Agent Evaluation vs Model Evaluation: What’s the Difference and Why It Matters

Agent Evaluation vs Model Evaluation: What’s the Difference and Why It Matters

Introduction As artificial intelligence systems become more complex and increasingly agentic, the distinction between model evaluation and agent evaluation has become both critical and nuanced. While the evaluation of underlying models (such as large language models, LLMs) remains foundational, the rise of AI agents (autonomous entities capable of multi-step

Top 5 Model Evaluation Tools to Improve Your LLM-Powered Applications

Top 5 Model Evaluation Tools to Improve Your LLM-Powered Applications

Large Language Models (LLMs) are at the heart of today’s AI powered applications, from chatbots and copilots to knowledge management tools. However, teams need to know: * Are outputs factually correct and safe? * Do they remain reliable across scenarios and updates? * Can we monitor issues in production before they spiral

AI Reliability: How to Build Trustworthy AI Systems

AI Reliability: How to Build Trustworthy AI Systems

Introduction Artificial intelligence is rapidly transforming industries, driving innovation, and redefining how organizations operate. However, as AI systems become more pervasive, the imperative to ensure their reliability and trustworthiness intensifies. Building trustworthy AI is not only a technical challenge, it is a multidimensional endeavor that encompasses ethics, governance, transparency, and