Generative AI Apps Need an Ecosystem, Not Just a Prompt

Every engineer has a moment early in their career when they realize the truth: You cannot learn everything. Much like doctors choose specialties, engineers eventually pick lanes. Backend, infrastructure, DevOps, mobile, frontend, machine learning systems. Not because we lack curiosity, but because there is simply too much to know. Technology evolves, interests shift, tools advance and our skills follow. Yet beneath all the specialization, there is a shared foundation. We all learned how variables work. How databases scale. Why sharding matters. Why staging environments exist. Why cloud providers give you credits when you are building your first app. There is a common layer of hard-won engineering wisdom — an ecosystem of guardrails that makes real software work in production. And this is exactly why vibe coding does not work. Generative AI apps today are mostly built through vibes. A little prompting here, some handcrafted reasoning overhead there, a sprinkle of “act like an expert” and suddenly it feels like you have an application. After working with thousands of businesses building AI features, I see this pattern constantly. Founders spend weeks perfecting a single prompt, testing it manually on cherry-picked examples, declaring victory when it works beautifully in their controlled environment. But prompts alone are not software. And vibe coding, for all its creative energy, breaks the moment you ask it to behave like production code. A real engineer would never ship an app to customers without the ecosystem around it. DevOps. Logging. Monitoring. Error handling. CI/CD pipelines. Regression testing. Versioning. A frontend that makes sense. A backend that does not fall over under load. Security layers. Rate limiting. Graceful degradation. Yet somehow we expect vibe-coded applications to do all this with nothing but a prompt and good intentions. We act shocked when they fail. What Production AI Actually Requires Building AI apps requires even more than traditional engineering, not less. The tools are different. The failure modes are weirder. The surface area is larger. Here’s what I mean: Evaluations systems: You need evals to understand whether your app is behaving correctly across thousands of edge cases. Not just the happy path you tested manually but the weird inputs users will inevitably throw at it. When a traditional app breaks, you get a stack trace. When a large language model (LLM) misbehaves, you get subtle wrongness that slides past your eyes until a customer complains. Continuous optimization: You need optimization because LLMs drift, contexts shift and prompts decay. What worked last month stops working this month because the model updated or user behaviour changed or your edge cases evolved. You need systems that detect this degradation and improve prompts automatically, not a founder frantically rewriting prompts at 2 in the morning because customer complaints are piling up. Memory and state management: You need memory so the app has continuity. Real applications remember context; they maintain state across sessions. You cannot build a useful AI feature that forgets everything between requests and expects users to re-explain their situation every time. Most vibe-coded apps do exactly this because state management is hard, and prompts don’t solve it. Observability: You need observability because hallucinations hide until they explode in a customer’s hands. You need to know when your AI is uncertain, when it’s making things up and when it’s degrading gracefully versus failing catastrophically. Traditional logging isn’t enough; you need specialized tooling for AI behaviour. Integration architecture: AI features don’t exist in isolation; they need to connect to your existing data systems. You need orchestration layers that let models, memory systems and data sources work together coherently. Without an ecosystem, your AI feature is a mood board pretending to be software. Why Demos Look Amazing Until Production Fails This is why so many AI prototypes feel magical in a demo and chaotic in production. Every day when I talk to customers, they are terrified because they know this is the future, and they want to use it, but real people’s jobs and livelihoods are on the line. Everything they put into production is at risk. The demo is controlled, and it has curated examples, while the prompt has been tweaked to perfection for those specific cases. Everything works beautifully because the environment is constrained and the test cases are known. Then it gets deployed to production, to real users with messy data. Suddenly, the perfect prompt is failing 30% of the time, and nobody knows why because there is no eval framework to measure it. It is not because founders lack talent or ambition. It is because we are treating AI applications as one-liners instead of systems. We are trying to vibe our way into production. We are skipping the entire engineering discipline that makes traditional software reliable and expecting AI to magically compensate through better prompts. Building the Missing Ecosystem If you want AI features to behave with the reliability of code, you need to give them what code has always had: structure, tooling, guardrails and continuous improvement. You need a platform approach, not a prompt approach. A platform that can evaluate, optimize, detect edge cases and improve handling over time. That can integrate models and memory and data into a coherent system that is observable and correctable. You need the same rigor you would apply to any production system. Version control for prompts and evals. Testing frameworks that run automatically. Monitoring that alerts when behavior degrades. Rollback capabilities when deployments go wrong. Documentation that explains not just what the prompt does, but why it is structured that way and what trade-offs it makes. You need team workflows that let multiple people contribute without stepping on each other. You need environments for development, staging and production. You need ways to experiment safely without breaking the live system. This is not theoretical. This is basic software engineering applied to a new domain. Security, compliance, reliability and quality because those fundamentals don’t go away just because it’s new technology. The companies building reliable AI features understand this. What Comes Next AI apps cannot succeed on vibes alone. They need the engineering ecosystem that has always made production software work. Vibes are great for creativity and exploration. They are terrible for reliability and scale. Real AI applications need specialties, tools and discipline just like every other branch of engineering. They need platform thinking and not prompt thinking. And the companies that embrace this reality are the ones that will build AI features that survive long past the hype cycle. The question for every founder building with AI right now is simple: Are you building a demo or a system? Because if you want your AI feature in production, you need to stop vibe coding and start engineering. The post Generative AI Apps Need an Ecosystem, Not Just a Prompt appeared first on The New Stack.