Design Review: Assumptions, Experiments, and Evidence Gates
Explore how to effectively review generative AI system designs by translating assumptions into testable hypotheses and defining evidence gates. Understand how to justify decisions on autonomy levels and architecture complexity through measurable commitments, risk assessment, and experiment planning. This lesson helps you prepare a review narrative that links assumptions to evidence needed for stakeholder approval in enterprise environments.
The solution paper is written. Before it moves forward, it should withstand review from stakeholders responsible for identifying gaps. Design reviews for GenAI systems should be grounded in evidence. Any move beyond the minimum sufficient step in the decision ladder should be justified by a measured gap and a planned experiment rather than a preference for greater autonomy.
In the mock design review of our copilot’s solution paper, the real question is whether the paper contains enough measurable commitments, realistic feasibility claims, and explicit oversight to warrant the next spend of time, integration effort, and risk acceptance.
A skeptical enterprise review board challenges five areas, and each has to be defensible in writing:
Measurability: Can the proposed KPIs and service level objectives actually be instrumented end to end?
Feasibility realism: Do the data access, identity, and change management assumptions hold under the stated constraints?
Risk and oversight sufficiency: Are privacy, security boundaries, auditability, and human approval points where they need to be?
Exit risk: Can model, vendor, and knowledge dependencies get replaced without rewriting the business process?
Cost per successful task: Does the unit economics account for failures, retries, and human review time, not just average latency or token cost?
The paper’s sections should all support the same level of evidence-based review: the problem and decision statement, the KPI table, the feasibility table, the decision-ladder step and its criteria mapping, the scorecard, the assumptions and evidence log, and the recommendation and requested decision. Governance and oversight commitments are distributed across the ...