Great AI Evals Mix Systems Engineering, Creative Direction and Adversarial Intuition
karinanguyen · x · 2026-10-23
The author offers a compact view of what building great evals actually takes: it blends systems engineering (rigorous pipelines), creative direction (designing scenarios that reflect real needs), and adversarial intuition (anticipating where models will fail or game the metrics). A distilled methodological take on agent eval engineering.
More from coding & agent
- Why Zig's explicit allocators make it a great fit for building databases — arpit_bhayani · 2026-10-23
- How to build a Company OS with Claude Code and Slack, after 11 days of building — aakashgupta · 2026-10-23
- Zapier CEO's AI fluency rubric: the 9-step roadmap from Capable to a Transformative rating — aakashgupta · 2026-10-23
- Smaug-Nemotron-3.5 debuts as a top US open-source agentic coding LLM that runs on a single machine — bindureddy · 2026-10-23
- A /yea hook to stop Claude from proposing obvious follow-up polish — ryunuck · 2026-10-23
- 15-year-old explains boss-bot routing in React Native multi-agent app Lunaar — Adept_Direction2591 · 2026-10-23