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A repair step built to fix narrated-beat framing in an AI ad pipeline kept producing the exact defect it was meant to prevent — plus critics that could detect identity drift but never repair it, grade a cutaway on criteria it was never shown, and a verdict describing frames that
Originally published at norvik.tech Introduction An in-depth analysis of frontier...
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
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.
In 2026, the average mid-size engineering organization runs somewhere between eight and fifteen...
Meaning has distance, and a clean answer can still sit far away from the truth of the...
Introduction Do you need to evaluate AI models with your data or cases? Yes, absolutely....
Evaluating AI-powered Chatbot Platforms for Customer Support Part 2: Advanced Analytics and Performance Metrics
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
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.
An LLM can explain one stack trace perfectly and still be the wrong model for your application. The...