#aievaluation

Introduction Do you need to evaluate AI models with your data or cases? Yes, absolutely....

Packaging a render-six-references-and-measure workflow into a reusable skill for an AI ad generator, and the audit backlog it surfaced: dead flags, a stop-ship brand gate, a misspelled brand name on a finished master, and a documentation culture that corrects its own wrong claims

Building a panel of AI critics that check a generated ad against itself — same product, same person, does the cutaway match its line, is the person alive — and the ways a critic itself can be quietly wrong: starved of slots, unable to fail, or grading a picture that will never re

Discover how a new evaluation harness revealed that LLMs often sound sure while delivering incorrect answers, and why enterprises must verify AI output before launch.

Google is launching the first double-blind tests for its Gemini AI, a cryptographic method to prevent companies from gaming their own performance scor

An LLM can explain one stack trace perfectly and still be the wrong model for your application. The...

In 2026, the average mid-size engineering organization runs somewhere between eight and fifteen...

VIDRAFT가 공개한 AX-Ray는 LLM의 인과누설(Causal Leakage) 취약점을 진단하는 AI 안전성 평가 프레임워크. 117개 진단 항목과 다국 법규 매핑, Hugging Face 리더보드 제공.

Meaning has distance, and a clean answer can still sit far away from the truth of the...

Evaluating AI-powered Chatbot Platforms for Customer Support Part 2: Advanced Analytics and Performance Metrics

A passing result can still be bad evidence. A coding agent can reach green tests after blind...

Agentic AI in production isn’t about autonomous loops — it’s about structure, evals, and auditable outputs. A guide drawn from real-world builds that scored 84% precision and matched 23,000 formations with evidence-quoted rationale.