How to Measure Success.

                                                                                                                                                                                                                                                                                                                                  Which is the bigger risk, an AI model that never ships, or one running in production that nobody is checking?                                                                                             
                                                                                                                                                                                                           The shelved model wastes engineering hours. The unmeasured one quietly erodes decision quality, burns cash on bad outputs, and accumulates invisible errors that compound over months. Most organisations pour their energy into the build. Almost none invest proportionally in knowing whether the thing actually works.                                                                                             

Evaluation-driven development offers a corrective. Borrowed from the discipline of test-driven software engineering, the idea is simple: define what success looks like before you write a single line of code. Then hold yourself to it, continuously.

The mechanics follow a three-stage loop; Specify,Measure, Improve.

First, specify. For every step in the workflow, the team articulates, in writing, what a correct output looks like. Not aspirational language about "better customer experiences." Examples: the right recommendation, the correct fraud flag, the accurate summary. If your team cannot describe a good answer, no model can reliably produce one.

Second, measure. Three approaches dominate in practice. LLM-as-a-judge, where a language model evaluates another model's output against defined criteria, is now used in over half of all production AI evaluations. It is fast, inexpensive, and can articulate its reasoning. The limitation is self-bias; models tend to prefer outputs that resemble their own style, which makes smaller, purpose-built judges arguebly more reliable than general-purpose ones. Human review remains essential for high-stakes decisions but is best deployed surgically: automate the bulk grading, reserve expert attention for the critical data. And crucially, slice the data. Aggregate accuracy scores mask segment-level failures. An 89% average can hide a pocket where one customer group receives consistently poor answers.

Third, improve and keep improving. Each failure pattern your evaluations surface becomes a permanent addition to your test suite. Fixes take many forms: revised prompts, adjusted retrieval logic, tighter guardrails. The evaluation itself may need updating as your understanding of the problem matures. The point is that the feedback loop never closes.

The pattern among organisations extracting durable value from AI is not technical sophistication. It is measurement discipline. They chose a success metric early; engagement lift, fraud losses avoided, code correctness. And then build the infrastructure to track it before they built the model.

The implementation of the AI is the easy part. Knowing whether it works is the hard part that actually matters.