A compelling GenAI demo can be built in an afternoon. A GenAI feature that thousands of people rely on takes the same engineering discipline as any other production system — plus a few new practices.
Evaluation before optimization
Collect a representative set of real inputs and define what good output looks like. Automate scoring using a mix of exact checks, heuristics and model-graded rubrics. Run the suite on every prompt, model or retrieval change — treat it like a regression test.
Guardrails
- Input validation and prompt-injection defenses
- Structured outputs with schema validation
- Content and PII filters
- Clear fallbacks when confidence is low
- Human review for high-impact actions
Observability
Log prompts, retrieved context, model versions, latency, token usage and user feedback for every request, with appropriate privacy controls. When quality drifts, you need to see why.
Cost and latency budgets
Set explicit targets per request. Use caching, smaller models for simpler sub-tasks, streaming responses and batching where possible. Monitor spend per feature and per customer.
Design the UX for uncertainty
Show sources, allow easy correction, and make it clear what the AI did. Users trust systems that are transparent about their limits.
Production AI is 20% model and 80% engineering around it.