
Vibe Coding to Production: Building AI Apps That Actually Scale
The AI Forecast: Data and AI in the Cloud EraShow Notes
Now that AI coding tools have put development capabilities into more hands, prototypes are becoming business-critical applications almost overnight. Shanea Leven sees an opportunity for a new generation of builders, provided the infrastructure around their applications keeps pace. In this episode of The AI Forecast, Paul Muller sits down with Shanea Leven, co-founder of Empromptu AI, to explore what it takes to build production-ready AI applications. Shanea explains how organizations can give developers and new technical employees room to build while maintaining the standards required for enterprise software. Shanea also explores the infrastructure surrounding generative AI applications, including the need to detect drift and evaluate outputs as real-world data flows through a system. As she puts it, the familiar rule still applies: “Garbage in, garbage out.” In their lively discussion, the pair covers:
What separates a working AI app from a production-ready one How governance should be built into AI development Why organizations should fingerprint applications before production How non-technical subject matter experts can become effective AI builders How AI coding changes the role of software engineers What companies should capture today to prepare for custom AI models
The conversation points toward a future where more people can build software themselves. Engineering teams will play a critical role in making that possible by creating the guardrails that allow ideas to move safely from a prompt into production. Want to hear more about AI governance? Check out Ep 81 | AI Guardrails: How to Govern AI Without Slowing Innovation. Stay in touch with Shanea: Shanea Leven on LinkedIn: https://www.linkedin.com/in/shaneak/ +++ Like and subscribe to The AI Forecast, sponsored by Cloudera, to stay up to date on the latest episodes. You can watch the video version of this episode on Cloudera’s YouTube Channel.
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