Most GenAI demos look great. Production is where things break.
I have shipped GenAI features into real products - not hackathon demos, not internal tools, but user-facing features that needed to work reliably under load. The gap between a working prototype and a production system is wider in AI than in any other area of software engineering. Here is the checklist I use to cross that gap.
Evals come first. Before you optimize prompts, before you fine-tune, before you do anything, you need a way to measure whether the model is doing what you want. I build evaluation datasets from real user inputs, label the expected outputs, and run every prompt change against this dataset. Without evals, you are tuning by vibes. Vibes do not scale.
Prompt robustness is the second item. Your prompt will encounter inputs you did not anticipate. Users will paste in entire documents, send empty strings, include emoji, mix languages, and try to jailbreak the model. I stress-test prompts with adversarial inputs before launch. The goal is not perfection. It is graceful handling. The model should produce a useful response or a clear refusal, never garbage.
Safety rails are non-negotiable. I run model outputs through a validation layer before showing them to users. This includes content filtering, format validation, and domain-specific checks. If the model is supposed to return JSON, I parse and validate the JSON. If it is supposed to generate a product description, I check that it does not contain competitor names or inappropriate content. Trust but verify.
Latency budgets matter more than you think. Users expect web interactions to complete in under two seconds. A typical LLM API call takes 1-5 seconds depending on output length. I set a hard latency budget per feature and design around it. Streaming responses help. Caching identical queries helps more. Pre-computing common responses helps most. If the feature cannot meet the latency budget, it is not ready for production.
Fallback strategies are where most teams skip and most users suffer. The model API will go down. It will hit rate limits. It will return errors. I design every GenAI feature with a graceful degradation path. Sometimes that is a cached response. Sometimes it is a simpler rule-based system. Sometimes it is a message that says the feature is temporarily unavailable. The worst outcome is a blank screen or a spinner that never resolves.
Observability closes the loop. I log every model call - the input, the output, the latency, the model version, and whether the user accepted or rejected the result. This data feeds back into the eval dataset. It reveals prompt failure modes you never anticipated. It shows you which queries are slow, which are failing, and which are producing outputs that users override. Without observability, you are flying blind after launch.
The meta-lesson is this: GenAI in production is not an AI problem. It is a systems engineering problem. The model is one component in a system that includes validation, caching, monitoring, fallbacks, and feedback loops. Get the system right, and the model does not need to be perfect. Get the system wrong, and even a perfect model will fail your users.