| Measure | Score / 10 |
|---|---|
| buyer_fit | 9 |
| depth | 8 |
| search_terms | 9 |
| sources | 8 |
Practical, transparent instrument with auditable scoring and non-compensable blockers. Its validity is procedural rather than predictive, and that limitation is explicit.
Editorial self-assessment
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