How do I know if my AI product is ready to launch?
Your AI product is ready to launch when it passes a structured readiness checklist covering output quality, error handling, privacy, security, and monitoring. The practical test: run 50+ diverse real-world inputs through your product, watch 3–5 real users use it without your guidance, and verify you can detect and respond to failures after launch. If you can do all three, you are ready for a limited launch.
What should I test before deploying an AI app to users?
Before deploying, test output quality across diverse and edge-case inputs, all failure states and error handling, prompt injection and adversarial inputs, latency and performance under load, data privacy (what user data enters your prompts), and your monitoring and alerting setup. Pay special attention to how your product behaves when the AI is wrong.
What is the difference between AI readiness and AI product readiness?
AI readiness asks whether an organization is prepared to adopt AI tools — strategy, talent, data governance, and leadership alignment. AI product readiness asks whether a specific product built with AI is ready to be used by real customers — output quality, error handling, user experience, privacy, security, and observability.
How do I handle AI hallucinations in my product before going live?
First, identify every feature where the AI outputs facts, numbers, or instructions. For each, test what happens when the AI is wrong. Add UI signals (disclaimers, "verify this", confidence indicators) where mistakes could harm users. For retrieval-based features, test what happens when the source data does not contain the answer.
Is a vibe-coded app safe to ship to users?
A vibe-coded app can absolutely be safe to ship — but it requires the same production readiness work as any AI product. Rapid AI-assisted development speeds up building, but it does not automatically cover error handling, security, cost controls, or monitoring. Run through the checklist before launch.
What does a pre-launch checklist for an AI app include?
A pre-launch checklist for an AI app should include: output quality testing across diverse inputs, error state and fallback design, hallucination handling, prompt injection testing, data privacy and security audit, latency and performance testing, cost-per-user calculation, user feedback mechanism, monitoring and alerting setup, and a staged rollout plan.
How many users should I test with before a full launch?
For most AI products, start with 5–20 users in a closed beta, watch them use the product live or via session recordings, and collect structured feedback on output quality and usability. After addressing the major issues, open to 10% of your waitlist and monitor for 48–72 hours before expanding.
