What is the AI product engineering lifecycle?

The AI product engineering lifecycle is the end-to-end process of taking an AI idea into production, including strategy, data readiness, architecture choices, evaluation, deployment, monitoring, and continuous improvement.

What is the biggest reason AI products fail?

Most failures come from weak data readiness, lack of evaluation discipline, missing governance controls, or poor monitoring rather than model choice alone.

When should I choose RAG instead of fine-tuning?

Choose RAG when answers depend on private or frequently updated knowledge and you need grounded responses. Choose fine-tuning when you need consistent output format or stable behavior across many requests.

What should I measure after launch?

Track quality, drift, latency, cost per request, fallback rates, escalation rates, and user satisfaction signals. Add retrieval metrics if you use RAG.