AI Requirements Engineering

A requirement states a needed system property or behavior and connects it to a verification or validation method. AI requirements combine deterministic rules, such as permitted actions and response schemas, with statistical expectations assessed over defined cases. They must identify the population and context in which quality is measured. This differs from product management's selection of value and scope, and from prompt writing's configuration of one model interaction. The specification covers the complete system, including retrieval, tools, interface and human workflow, rather than assuming a model's general capability becomes an application guarantee.

The practitioner elicits concrete tasks and unacceptable outcomes, records assumptions and resolves ambiguous terms through examples. They define inputs, outputs, data availability, permission boundaries and error behavior. For variable outputs, they choose a representative evaluation set, review criteria and release conditions. They trace requirements to implementation and checks, assigning owners for unresolved questions. Useful work yields an actionable specification and acceptance plan that engineering, design and reviewers can apply consistently, including what the system should do when it cannot satisfy a request.

Terms such as accurate, helpful or safe remain ambiguous without context and checks. A benchmark threshold can hide failure on important subgroups or rare consequential cases. Overly narrow acceptance can reward formatting while missing the real user need. Inspect traceability, evaluation coverage and fallback requirements. Requirements cannot make a probabilistic model deterministic; they define acceptable system behavior and the evidence needed to decide whether a particular implementation meets it.