Evaluation Driven Development and AI ROI

The typical AI project follows a recognizable pattern. Pick the most capable model available. Build something that demonstrates what it can do. Present it to stakeholders. Somewhere around month four, start asking whether it is actually solving anything.

The demo impresses people in the boardroom. Then deployment reveals the real questions: how much does each inference cost at volume, what happens when the output is wrong, and what does 'wrong' even mean for this use case. Those questions should have been answered before the first API call.

Evaluation-Driven Development flips the sequence. Before writing any code, you define what success looks like in specific, measurable terms. Not 'the AI handles customer support.' Something like: reduce refund request tickets by 15 percent, match policy documents 95 percent of the time, cost under ten cents per resolution.

Those constraints are not arbitrary. They are the finish line. Every architectural decision, every prompt iteration, every evaluation run points back to them. The project has a definition of done from the beginning.

The approach is borrowed from test-driven development in software engineering. In TDD, you write the test before you write the code. In EDD, you write the evaluation criteria before you choose the model. The logic is the same: a system built toward a defined measure behaves more predictably than one built toward a vague goal.

The practical effect is that teams catch misalignment early. If a candidate approach performs well on the demo but cannot hit the cost constraint, that failure surfaces in week two instead of week twelve.

Teams that define their success metrics before building ship faster, iterate more clearly, and build AI systems that continue to justify their cost in production. The ones that skip this step tend to discover their actual requirements six months in, after they've already built the wrong thing.