A demo proves something can work once. A product proves it works reliably, for strangers, on a bad day. The distance between them is where the real work lives.
Evaluation, not vibes
You cannot improve what you do not measure. Production AI needs an evaluation harness — real cases scored automatically — so quality is a number you watch, not a feeling you hope for.
Guardrails and graceful failure
The question is not whether it will be wrong, but what happens when it is. Confidence thresholds, clean human handoff, and an honest “I do not know” beat confident hallucination every time.
Observability
You need to see what it did and why — retrieval, prompts, outputs — or you are flying blind the moment it drifts in production.
Production-ready AI is mostly unglamorous engineering: evaluation, guardrails, observability. The model is the easy part.
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