An AI demo optimises for surprise: a prompt produces a useful answer in a carefully selected example. A production system optimises for repeatability, bounded cost, acceptable latency, and safe behaviour when inputs are incomplete or adversarial. Confusing those goals is why many promising pilots never become dependable products.
Start by defining the decision or workflow the system supports. Write down what a good outcome means, what must never happen, and where a human must approve the result. Then build an evaluation set from real traffic, including difficult cases, not just examples that make the model look good. Without this baseline, every model change becomes an opinion contest.
The engineering checklist includes structured outputs, schema validation, prompt and model versioning, retrieval quality, permission-aware tool access, redaction, rate limits, and audit logs. Treat external model calls as unreliable dependencies: use timeouts, retries with limits, queues for long work, and a fallback path that is useful rather than merely polite.
Cost and latency need first-class budgets. Track tokens, cache hit rates, model choice, queue time, and human review rates by workflow. A cheaper model that causes more manual correction may be the expensive option. Conversely, a larger model may be justified for a narrow, high-value decision if its quality is measurable.
The final gate is operational ownership. Someone must own the evaluation set, incident response, data retention, provider changes, and rollback process. AI becomes a production capability when it has the same discipline as payments, identity, or any other critical subsystem.
Frequently asked questions
When is an AI demo ready for production?
When its success criteria, failure modes, evaluation set, security boundaries, cost budget, and operational owner are explicit and tested against real examples.
Do we need a fine-tuned model?
Not necessarily. Retrieval, tool design, structured outputs, and evaluation often deliver more value before fine-tuning is justified.
How should AI quality be measured?
Use task-specific evaluations combining automated checks, curated examples, production feedback, and human review for high-impact outcomes.
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