Learn · Field craft
AI evals in production for field engineers
In customer environments, reliability is an evaluation problem before it is a model problem. This page is the FDE-facing framing; the deeper technical field guide lives under Tech.
Production agent reliability is primarily an evaluation and operations problem. Frontier-lab FDE loops (see Anthropic and OpenAI) punish candidates who only talk about clever prompts.
What “good” looks like on a customer account
- Golden scenarios that mirror real operator tasks — not curated demos
- Rubrics for groundedness, task success, safety, latency, and cost
- Offline gates before any autonomy expansion
- Online monitoring with clear rollback / kill-switch owners
Field sequence FDEs actually run
- Shadow the workflow — find where time and risk live
- Fence the corpus / tools — permissions before prompts
- Write the golden set — 20–50 scenarios beat a thousand random chats
- Split failure modes — retrieval vs generation vs tool errors
- Promote with gates — quality + cost + override rate
Deep technical guide
For inner-loop vs outer-loop eval design, monitoring signals, and Day-2 drift handling, use the full field guide: Agent evals & monitoring. Pair with Enterprise RAG when retrieval is the bottleneck.
Interview / portfolio angle
In resumes and decomp rounds, show that you measured outcomes — not that you “added RAG.” Practice with case / decomp and the Translation Matrix.
Frequently asked questions
- Why do field engineers need AI evals?
- Demos hide retrieval failures, permission leaks, tool mistakes, and cost blowups. FDEs need golden scenarios and promotion gates so customer rollouts do not depend on prompt vibes.
- What is a good eval harness for an enterprise AI pilot?
- A small golden set tied to business outcomes, rubrics for groundedness and task success, offline checks before release, and online monitoring for drift, latency, cost, and override rates after launch.
- How do Anthropic and OpenAI standards show up in FDE interviews?
- Interviewers often probe whether you can separate retrieval vs generation failures, define quantitative rubrics, and refuse to expand autonomy without eval gates — not whether you trained the frontier model.