AI Engineer

The AI engineer ships: taking a promising model or prototype and making it fast, cheap, reliable, and safe enough for real users. It's software engineering with a probabilistic component — evals replace unit tests, and "works on my prompt" isn't done.

Overview

Production AI work is a loop: define the task and success metric, build an eval set, prototype with the strongest model, then optimize down the cost/latency curve while holding quality. Guardrails bound the failure modes; monitoring catches drift; iteration never really stops because models, data, and user behavior all move. The engineer's edge is measurement — every decision backed by the eval set.

When to use

• Turning a prototype prompt or agent into a user-facing feature.
• Choosing between models: quality vs. cost vs. latency trade-offs.
• Adding reliability: evals, fallbacks, guardrails, and monitoring.
• Debugging production AI issues: quality drops, cost spikes, weird outputs.

Core concepts

• Task definition: the feature framed as inputs, outputs, and a measurable success criterion. "Helpful summary" becomes "summary covering all 5 key points, under 150 words, faithful to source."
• Eval-driven development: a fixed set of representative cases with graders, run on every change. The equivalent of a test suite for probabilistic systems.
• Model routing: strong model for hard cases, cheap model for easy ones; classifiers or heuristics route. Quality where it matters, cost where it doesn't.
• Latency engineering: streaming, prompt compression, smaller models, caching, parallel calls. Users feel every 500ms.
• Guardrails: input validation, output filtering, topic boundaries, PII handling, and escalation paths. Designed per failure mode, not bolted on.
• Observability: log prompts (redacted), outputs, latencies, costs, and user feedback per request. Drift shows up in the metrics before users complain.

Practical workflow

1. Define the task contract and build the eval set (30+ real cases) before optimizing anything.
2. Prototype with the best available model; establish the quality ceiling and baseline cost/latency.
3. Optimize down the curve: try smaller models, compressed prompts, caching — keep whatever holds eval scores.
4. Add guardrails for the top failure modes found in evals and red-teaming.
5. Ship behind a feature flag with full logging; watch quality, cost, and latency dashboards.
6. Iterate: user feedback → new eval cases → prompt/model changes → re-eval → rollout.

Production checklist:
[ ] Eval set with graders, run in CI
[ ] Cost per request budgeted + alerted
[ ] p95 latency target met (streaming where needed)
[ ] Guardrails for top failure modes
[ ] Redacted logging + dashboards
[ ] Rollback plan for model/prompt changes
[ ] Human escalation path defined

Common pitfalls

• Shipping the prototype prompt: the demo prompt is rarely the production prompt. Harden with evals first.
• Single-model thinking: one model for everything. Route by difficulty; the savings are large.
• No cost tracking: per-request costs look tiny until scale. Budget and alert from day one.
• Eval-free iteration: tweaking prompts by feel in production. Every change goes through the eval set.
• Ignoring the tail: average quality is fine; the worst 5% of outputs cause the incidents. Study failures, not averages.
• Model updates as surprises: providers update models; behavior shifts. Pin versions, re-run evals on change.