Readiness quiz

4. Are the labels and examples trustworthy?

Frontier Development research deskSection 08 · Page 6 of 20Last updated 26 October 2026

A — 0: Labels are generated or inherited without checking. B — 1: A sample was reviewed, but disagreements or missing categories remain untracked. C — 2: A label guide, adjudication process and dataset-quality report exist for the target task.

Evidence to attach: reviewer instructions, disagreement examples and remediation list. If weak: repair labels before scaling the dataset. More examples of inconsistent behaviour can make the decision harder to interpret.

Question 4 of 12

Are the labels and examples trustworthy?

Evidence to show: reviewer instructions, disagreement examples and remediation list.

Read the full question →

A desk-designed readiness rubric, not a validated prediction of success. Progress stays in this browser; no answers are sent to the desk.

TRACE THE EVIDENCE

Section source library

This page contains no direct external citations. These are the section’s references; they do not independently validate the desk’s proposed scoring or methods.

Demystifying evals for AI agents — Anthropic, 9 January 2026 →www.anthropic.com
Llama 3.3 Community License Agreement — Meta →github.com
AI Risk Management Framework — NIST →www.nist.gov
RouteLLM: Learning to Route LLMs with Preference Data — Ong et al., revision 23 February 2025 →arxiv.org
A Field Guide to Rapidly Improving AI Products — Hamel Husain →hamel.dev
GroupKFold — scikit-learn documentation →scikit-learn.org
Pricing — Together AI, snapshot checked 26 October 2026 →www.together.ai
The Inference Engineering Masterclass — Philip Kiely and Ali Taha, Latent Space, 3 August 2026 →latent.space
The Open Source AI Definition 1.0 — Open Source Initiative →opensource.org
SB26-189 Automated Decision-Making Technology — Colorado General Assembly →leg.colorado.gov
Guidance on HIPAA & Cloud Computing — HHS Office for Civil Rights →www.hhs.gov