LLM-as-a-judge is an evaluation approach where a language model is used to assess the quality of another model’s output. Instead of relying solely on human annotators, an LLM is prompted to evaluate a response according to predefined criteria such as correctness, helpfulness, or relevance.

This guide walks through everything you need to know about testing conversational AI from the perspective of a developer who needs to ship a production-ready system. You'll learn what makes these systems unique to test, which metrics actually signal quality versus noise...

The shift AI has brought to software development goes beyond coding assistants and faster deployments. The more fundamental change is that the people who understand the problem domain can no longer sit on the sidelines.

It started the same way many of my engineering mistakes begin: with a beautifully over-designed document. I had spent hours writing a lengthy, thoughtful Product Requirements Document (PRD) for our Model Context Protocol (MCP) integration...

We just hosted our first Community Hour, a new regular virtual meetup for everyone building, testing, and evaluating Gen AI agents and LLM applications. Join our growing community where testing is a collaborative conversation, not an afterthought.

A behind-the-scenes look at how we made Rhesis run anywhere and what we learned along the way. It started with a simple question from our first Objectives & Roadmap session: "Can I run Rhesis on my laptop without dealing with cloud credentials?"

Discover how Rhesis AI pivoted from enterprise SaaS to open source, what drove the rebrand, and the lessons every AI startup can learn about aligning brand, product, and community.

Artificial Intelligence (AI) is transforming numerous sectors, profoundly impacting task performance and decision-making processes. However, as AI's prevalence increases, so does the need for trustworthiness, i.e., ensuring that AI applications operate as intended and meet required quality standards.

As Gen AI technology, particularly Large Language Models (LLMs), continues to shape industries across sectors, it is crucial to understand how these applications perform in real-world scenarios and assess their overall quality and trustworthiness.