Shipping is only the beginning for generative AI applications. These systems require robust evaluation, observability, and ongoing iteration to function at scale. Early-stage experimentation -- vibe prompting -- uses off-the-shelf models and playground tools to test viability. The next stage requires building trust by addressing reliability, legal, and security issues that can stall deployment. Mature operations focus on continuous improvement by detecting failures at scale, fixing them rapidly, and iterating without regressions. Defining what good looks like, mapping failure scenarios, setting quality metrics, and integrating telemetry and human-in-the-loop reviews are essential practices for production-ready AI.
Most AI projects follow a predictable maturity curve. In the earliest stage - what Cairns calls vibe prompting - teams experiment rapidly, using off-the-shelf models and playground tools to see if an idea works. Success at this stage is defined loosely: "Does it run? Does anyone care enough to use it?" If the answer is yes, the challenge shifts to trust. Can the system operate reliably enough to be deployed in production?
Many projects stall here - sometimes in legal review, sometimes with security concerns, sometimes because manual testing can't keep up with the volume of edge cases. Teams that do cross this threshold find themselves at a third stage: continuous improvement. Now the question becomes: Can we detect failures at scale, fix them quickly, and improve without breaking what works? This is where evaluation frameworks, online telemetry, and human-in-the-loop reviews become essential.
Collection
[
|
...
]