From a promising AI demo to a dependable production system
A public-safe view of the architecture and operating practices that make an AI product reliable beyond the model.
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Production AI · Architecture · Reliability
Reliable AI systems, built for the real world.
I’m Christian Yongwhan Lim. I architect the evaluation, observability, workflow state, and recovery that turn ambitious AI products into dependable production systems.
Chief Architect at Probook. Previously six years across Google Research and ML infrastructure; also an Adjunct at Columbia and Director of Internships at the ICPC Foundation.
New York, 2026
What changes
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- Define the outcome Replace model-centric success with end-to-end operating criteria
- Instrument reality Connect scenario evaluation and traces to failures users experience
- Design recovery Make fallback, handoff, rollback, and operating ownership explicit
How I can help
Choose the operating problem
My primary practice is production AI reliability. I apply the same operating discipline—clear outcomes, observable evidence, and recoverable systems—to technical learning and institutions.
01 · Production AI
Make a production AI system dependable
Architecture, evaluation, observability, workflow state, latency, and recovery across the full system—not only the model.
Discuss the system → 02 · Technical learningBuild a technical-learning environment
Curriculum, deliberate practice, coaching, feedback loops, mentorship, and durable community leadership.
Compare approaches → 03 · SpeakingBring the work into a room
Speaking and substantive collaboration on AI reliability, engineering leadership, problem solving, and technical education.
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Public evidence
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Methods and cases you can inspect
01 / Production AI
02 / Decisions
03 / Reliability
From a promising AI demo to a dependable production system
A high-level case study in turning model behavior into an operable system through explicit outcomes, scenario evaluation, observability, workflow state, and recovery.
System boundary · evaluation · observability · recovery · operating ownership Read the high-level case study →Production decision framework
Choose the least complex system that clears the operating bar across quality, cost, risk, time, and reversibility.
Objective · constraints · alternatives · evidence · review · revisit triggerUse the framework →Contest engineering
Adversarial validation and release discipline for high-stakes programming contests.
Specification · independent solutions · failure catalogue · data · CI · judge reviewUse the release field guide →“He has had a bigger impact in that short time than almost anyone has had in the history of North America.”
Working Notes
Writing test generators that falsify your assumptions
Design generated data around named failure hypotheses, structural witnesses, reproducible randomness, and evidence that each family has a job.
Use the generator guide →What fifteen years in the ICPC changed about how I solve problems
Reflections across competing, coaching, judging, organizing, and teaching—and what those roles reveal about the real work of problem solving.
Read the note →How I review an ICPC contest with a team
The scoreboard records outcomes. A useful review reconstructs the decisions, failure modes, and team interactions that produced them.
Read the note →