Insights

Notes on evaluating AI systems before they meet real users.

A four-part series on choosing AI testing tools — written because “what’s the best eval tool?” is the wrong question.

• Note · 1 of 4

  There is no best AI testing tool

  A dashboard that scores prompts is not a red-team tool. Measurement is not assurance.

• Note · 2 of 4

  Name what you are testing before you pick a tool

  Model, prompt layer, retrieval, end-to-end, agent trajectory, production. Six different objects.

• Note · 3 of 4

  A practical map of AI testing tools

  The landscape by job to be done, not by marketing category — with shortlists you can actually start from.

• Note · 4 of 4

  How to choose without getting sold a dashboard

  Name the release decision, write the contract, calibrate the judges, then test the measurement system itself.

Practical notes on testing retrieval, grounding, hallucination and adversarial behaviour.

• Guide

  How to Detect and Prevent AI Hallucinations in RAG Systems

  Learn how to detect and prevent AI hallucinations in RAG systems with evidence-based testing, grounding assessments, and actionable remediation plans.

• Guide

  Eval Ops Explained: Running AI Evaluation at Scale

  Learn what Eval Ops is, why it matters for production AI systems, and how structured evaluation operations improve reliability, accuracy, and trust at scale.

• Guide

  Adversarial Testing for AI Applications: What You Need to Know

  Learn how adversarial testing uncovers hidden vulnerabilities in AI applications and why it's essential for building reliable, production-ready AI systems.

• Guide

  RAG Accuracy and Grounding Assessments: A Complete Guide

  Learn how RAG accuracy and grounding assessments work, why they matter for AI reliability, and how specialized testing uncovers retrieval and context failures.

• Guide

  Independent Verification of AI-Built Applications: Why It Matters

  Why independent verification of AI-built applications matters for reliability, safety and trust, and how structured testing finds the blind spots.

• Guide

  AI Test Tooling: How to Build a Smarter Evaluation Stack

  Explore how advanced AI test tooling strengthens your evaluation stack, improves reliability, and surfaces issues before they reach production.

• Guide

  Retrieval Failures in AI Systems: What Goes Wrong and Why

  Learn why retrieval failures happen in AI systems, how they affect output quality, and what evidence-based strategies help you detect and resolve them.

• Guide

  How to Build a Reliable AI Testing Strategy from the Ground Up

  Learn how to build a reliable AI testing strategy that covers evaluation, grounding, hallucination detection and verification for production AI systems.

• Guide

  Understanding AI Grounding Failures: Causes and Fixes

  Learn what causes AI grounding failures, how they silently undermine system reliability, and the steps you can take to detect and fix them before they escalate.

• Guide

  Measuring AI System Reliability: Key Metrics and Methods

  Learn how to measure AI system reliability with the right metrics and methods, and how structured testing reveals performance gaps before they become critical.

• Guide

  What Is RAG Pipeline Evaluation and Why Does It Matter?

  Learn what RAG pipeline evaluation involves, why it's critical for AI reliability, and how to assess each component for accuracy and performance.

• Guide

  From Prototype to Production: AI Application Verification Explained

  Learn how AI application verification bridges the gap between prototype and production, so you can decide whether your AI system is ready to deploy.