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Explain the AI product lifecycle from ideation to production.
This question sounds broad because the interviewer wants to see whether you can impose a release discipline on a probabilistic system. A weak answer gives the normal software lifecycle. A strong answer explains where AI products need extra gates: evaluation, rollout control, fallback behavior, and post-launch learning.
01Interview Context
This question sounds broad because the interviewer wants to see whether you can impose a release discipline on a probabilistic system. A weak answer gives the normal software lifecycle. A strong answer explains where AI products need extra gates: evaluation, rollout control, fallback behavior, and post-launch learning.
02The 90-second answer
I break the lifecycle into five stages: problem framing, thin-slice prototype, evaluation, production hardening, and continuous improvement. The AI-specific wrinkle is that a prototype that looks impressive in a demo is often still unsafe to ship. Before launch I want a golden dataset, a baseline to compare against, prompt or model versioning, staged rollout, and monitoring for quality, latency, cost, and safety.