Building production-grade AI features (beyond the demo)

Why AI demos fail in production

LLM demos look magical in a 5-minute screen-share. In production they face ambiguous user input, cost ceilings, latency SLAs, and compliance rules. The gap between demo and product is where most AI projects stall.

The checklist we use

1. Evaluate before you launch. Hand-label 50-100 realistic inputs and grade outputs on correctness, tone, and groundedness.
2. Always ground. For knowledge-heavy tasks, use retrieval (RAG) with citations, not raw prompts.
3. Cap cost and latency. Route cheap traffic to smaller models; reserve flagship models for the hard 10%.
4. Guardrails at the edges. Validate outputs with schemas (Zod / JSON schema) before they hit the UI or downstream systems.
5. Human-in-the-loop for high-risk paths. Don't let the model send money or delete data without review.

Closing

AI features don't win because the model is clever. They win because the team around the model is disciplined about evaluation, cost, and safety.