Principal Engineer shipping LLM systems

You can wire up an LLM and demo something that wows the room. Then it meets production and everything you weren't taught shows up at once: cost, latency, confident-wrong output, no evals, no traces. In this 3-hour working session you bring your real or planned LLM/agent feature, map its path to production with a Principal Engineer, and leave with a one-page production-readiness plan.
Walk in with a demo, walk out knowing exactly what it takes to put it in production, and a written plan to get there.
Decide what to hand the model vs keep in deterministic code.
A per-request cost and latency ceiling, and how to design to it.
The likeliest ways it breaks, plus the guardrails that contain them.
How you'll know it works before users do, and trace what it did.
Frame the system: draw the code-vs-model boundary for your feature and name the one job it must do reliably.
Set a budget: a per-request cost and latency ceiling, and the design choices that keep you under it.
Map failure: your top 3 failure modes and the guardrails, retries, and fallbacks that contain them.
Start evals and tracing: the smallest harness that tells you quality moved before your users do.
Leave with a one-page production-readiness plan and a 30-day next-steps checklist.

Principal Engineer, 500+ mentees, 16 years in production
An engineer suddenly asked to ship an LLM feature, who can build a demo but hasn't taken one to production.
A senior or staff engineer who owns an AI system and wants rigor on cost, failure modes, and evals before it ships.
A tech lead, founding engineer, or EM accountable for an AI feature's cost and reliability, who needs a clear framework to pressure-test.

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