The demo works. The model answers questions coherently, handles edge cases gracefully, and impresses everyone in the room. Three months into production, the same system is producing wrong answers on a predictable subset of inputs, costs three times what was projected, and has no mechanism to detect when it is failing.

This is not an Azure OpenAI problem. It is a production gap that shows up regardless of which model or provider you use. The demo surfaces the capability. Production surfaces the infrastructure you did not build.

Evaluation

A demo is not an evaluation. It is a curated selection of inputs that work well. Production is every input, including the ones you did not think of.

An evaluation harness is a structured set of test cases that measures model output quality against defined criteria. It tells you when a prompt change improves the outputs you care about and when it makes other outputs worse. Without one, every change to the system is a gamble.

Building the eval harness before you go to production is not optional work that can be added later. Once the system is live, you lose the ability to define "correct" from a neutral position. Users adapt to whatever the system does, and the definition of correct drifts toward "what it currently does."

Cost instrumentation

Token costs are predictable in a demo where you control the inputs. In production, users find ways to send inputs you did not anticipate, context windows fill up faster than projected, and retrieval systems add tokens to every query.

If cost observability is not in place on day one, the first signal you get about cost problems is a bill. By then, the usage pattern is established and changing it creates user friction. Instrument token usage per request type from the first day of production. Set budget alerts. Know your cost-per-query for each use case before you scale.

Drift

Language models do not degrade gracefully. They produce confident wrong answers on inputs that have drifted outside the distribution they were tuned for. Without monitoring for output quality over time, the model can be failing a meaningful fraction of queries for weeks before anyone notices.

Drift monitoring means periodically sampling production outputs and running them through the eval harness. It means having an alert that fires when quality metrics drop below a threshold. It means having a response plan that does not start with "restart the project from scratch."

The question to ask before you start

Before any AI engagement, the question is not "can the model do this?" The demo already answered that. The question is "what does production look like?" If the answer does not include an eval harness, cost instrumentation, and drift monitoring, the project is not production-ready regardless of how good the demo looked.

The goal is not a demo that works most of the time. The goal is a product that works consistently, or tells you clearly when it does not.