Eval-Driven Marketing

Marketing teams change prompts ad hoc — one more sentence of tone here, an extra rule there. Without test coverage nobody notices that the same change breaks brand rules elsewhere or distorts pricing statements. Eval-driven marketing therefore introduces an evaluation suite: a set of realistic queries with expected response properties, checked by rules, classifiers and model-based grading. Typical criteria are factual fidelity to the knowledge base, adherence to claims and compliance requirements, tone, language and formatting. Run automatically on every change, the suite delivers the same safety net as regression testing in software engineering.

Once an assistant speaks in the brand's name, every uncontrolled change is a reputation and liability risk. An evaluation suite makes quality measurable, enables faster iteration with less fear and simultaneously produces the documentation compliance and auditors expect.

An energy provider maintains 180 test cases for its tariff assistant, including trick questions on price guarantees. A model migration passes 174 cases but fails six with an incorrect notice period. The change is halted and the knowledge base corrected — a potential legal error in customer communication is caught before release.

Evaluation frameworks emerged from 2023 in ML engineering but long stayed inside technical teams. With assistants deployed in customer communication and the EU AI Act transparency duties from 2025, pressure grew to evidence quality in marketing too. By 2026 it became normal to treat prompt changes like code: versioned, reviewable and automatically tested. The trigger was several publicly discussed cases where assistants communicated wrong prices or impermissible claims.