AI Testing & Evaluations articles.
Independent evaluation and testing for LLMs, agents and chatbots — benchmark scoring, guardrail and bias testing, and continuous regression evals.
Latest in AI Testing & Evaluations.
RAG Evaluation Metrics Explained: Groundedness, Attribution and Context Relevance
A RAG system that gives a wrong answer could have retrieved the wrong documents, retrieved the right documents and ignored them, or retrieved the right documents and misread them. A single pass/fail accuracy score can’t distinguish between these — which is exactly what component-level RAG metrics are for.
Read article →LLM-as-a-Judge: Building Reliable Automated Evaluation Pipelines
Human review doesn’t scale to the volume of outputs a production LLM application generates. Using a second LLM to score the first one’s outputs scales fine — the question is whether the resulting scores are actually trustworthy, which depends entirely on how the judge is set up.
Read article →Continuous Evaluation Pipelines: Catching LLM Quality Regressions Before Your Users Do
An LLM application doesn’t throw an exception when it gets quieter, less accurate, or less helpful — it just does, silently, and the first signal is often a user complaint rather than a test failure. Continuous evaluation closes that gap.
Read article →Red-Teaming Your AI Agents: A Practical Guide to Adversarial Testing Before Launch
Standard evaluation tells you how well an AI system performs on the inputs you expected. Red-teaming tells you what happens on the inputs you didn’t — and for agentic systems that can take real actions, that gap is exactly where the serious incidents live.
Read article →LLM Hallucination Testing: Benchmarks, Metrics and Mitigation Strategies
“What if it just makes something up?” is usually the first question a risk committee asks about any LLM deployment — and it deserves a measured answer, not a reassurance.
Read article →How to Evaluate an LLM Application Before It Ships
Shipping an LLM-powered feature without a structured evaluation process is the AI-era equivalent of shipping code without tests — it might work fine in the demo and fail in ways you didn’t anticipate in production.
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