Most AI projects fail somewhere between demo works and production ships . The gap is rarely the model. It's the absence of the controls that turn a one-shot prompt into a system you can run, audit, and iterate on without setting fire to the budget. I made the chart above as the one-page version of the controls I would put on any AI team's first production sprint. Eight of them, organized by which side of the model they shape: Input, Reasoning, Output, Operations. Below is the why-each-matters and where teams typically get them wrong. Input Control: shape what goes in 1. Few-shot prompting Show the model two to five high-quality input/output examples instead of writing long instructions. The model picks up format, edge cases, and tone from examples in a way it does not from imperative prose. Five good examples beat five hundred words of "make sure to handle X, also Y, also Z." The mistake teams make is treating few-shot as a fallback when the system prompt isn't working. It's the opposite.…