AI Evaluation
AI evaluation establishes how much confidence a system has earned for the job it will perform.
A useful evaluation measures the system under conditions that resemble production and produces evidence strong enough to support the decision being made. That means testing more than headline accuracy. Repeatability, context, grading quality, orchestration, and failure behavior can all change whether a system performs reliably in real use. The articles here focus on building evaluations that show what an AI system can do, where its limits are, and how confidently it can be deployed.
5 articles
The Exam Has Errors. The Grader Changes Its Mind.
Every AI decision you have approved rests on an evaluation. Three findings say the evaluation is shakier than the model.
APR 23, 2026
One Thousand Identical Questions. Eighty Different Answers.
Temperature zero exists to make a model repeatable. It does not, and the reason is not randomness.
APR 9, 2026
128,000 Tokens. Eleven of Thirteen Failed at 32,000.
The advertised context window is a capacity. What the model can actually use is a much smaller number, and nobody prints it.
MAR 26, 2026
1,600 Traces. 14 Ways to Fail. None of Them Is the Model.
Multi-agent systems break on specification, coordination and verification. That list is an org chart, not a research problem.
MAR 12, 2026
74 Percent of New Pages Contain AI. The Tails Go First.
Training on generated data does not produce nonsense. It produces a model that has forgotten the rare cases.
FEB 26, 2026
About this topic
What good evaluation should tell you
A strong evaluation answers whether a system can reliably perform the specific job you intend to give it. That requires knowing what was tested, how success was defined, how results were graded, how performance changed across repeated runs, and whether the test reflects the environment the system will face after deployment.
Those details can materially change the conclusion. Performance can shift as prompts change, context grows, or multiple components begin interacting. Evaluating the complete system provides a more accurate view of production readiness than measuring the underlying model in isolation.
Measure the system you plan to deploy
Evaluation should reflect the architecture, data, tools, prompts, controls, and operating conditions that will exist in production. Test the situations that matter to the business, including difficult cases, repeated runs, longer context, tool failures, and interactions between components.
The evidence should also match the consequence of the decision. A low-risk assistant can tolerate more uncertainty than a system influencing financial transactions, customer commitments, access decisions, or regulated processes. Higher-impact use cases require stronger evidence.
How to use these articles
Start with the decision the evaluation needs to support. Define the capability being tested, the conditions under which it must perform, the acceptable level of variation, and the failures that would make deployment unsafe or ineffective.
Then examine the evidence behind the reported result. Review the test data, grading method, repeated-run performance, excluded cases, and surrounding system behavior. A strong evaluation gives decision-makers a clear view of what the system can handle, where human or deterministic controls are required, and how much confidence the deployment deserves.