Engineering notes
How we build AI-generated, quality-gated hiring assessments — the architecture, the trade-offs, and the honest parts.
Page 6 of 6
July 18, 2026 · 9 min read
Cognitive ability in hiring is a strong predictor of potential and the easiest pillar to over-weight. How to use the cognitive signal without an IQ contest.
July 18, 2026 · 10 min read
Situational judgement tests surface how a candidate decides under ambiguity, not what they recall. Why scenario-based evaluation predicts performance.
July 18, 2026 · 9 min read
Behavioural assessment turns working style into observable, comparable signals. How anchored rubrics replace 'culture fit' guesswork with real structure.
July 18, 2026 · 8 min read
A domain skills assessment measures the work itself, not a proxy. How per-role, systematically generated questions test real job skill fairly across candidates.
July 18, 2026 · 8 min read
Hiring fraud — cheated assessments, impersonation, and AI-ghostwritten answers — poisons every downstream decision. The real cost of hiring fraud, and how to fix it.
July 18, 2026 · 9 min read
NYC Local Law 144 requires an independent bias audit before an automated hiring tool screens candidates. What the rule covers, who owns it, and what it measures.
July 18, 2026 · 9 min read
The EU AI Act classes hiring AI as high-risk, with duties on documentation, human oversight, logging and transparency. What that means for your assessments.
July 18, 2026 · 9 min read
Adverse impact and the four-fifths rule decide whether your hiring passes a bias audit. How the 80% ratio works, its failure modes, and how to monitor it.
July 18, 2026 · 9 min read
GDPR for hiring: what candidate data rules require on lawful basis, consent and minimisation, and how to honour erasure while keeping an audit trail.
July 18, 2026 · 11 min read
AI-generated assessments compose a test per job instead of picking from a shared library. How generation, quality gates and compliance actually work.