A junior developer is asked to fix a bug in the new AI-powered pricing engine. The feature is simple enough: loyal customers should see slightly better discounts. But it’s misfiring, offering 50% off to users who signed up yesterday. "Where’s the spec?" she asks the product manager. He doesn't send a document. He forwards a link to a 200-message-long chat transcript between himself and the company’s internal AI assistant. "It’s all in there," he says. "We sort of tweaked the prompt until it felt right."
The specification is gone. In its place is a meandering conversation, a transcript of trial and error. This is the new reality in teams rushing to integrate generative AI, and it is a catastrophic failure of process masquerading as innovation. We are trading the discipline of software engineering for the immediacy of a chat window, creating a new and insidious form of technical debt.
A formal requirements document is built on precision. It contains verifiable statements, edge cases, and non-negotiable logic. If customer_creation_date < (NOW() - 365 days) AND purchase_count > 10, then apply discount_tier_A. This is testable. It is unambiguous. It can be handed to a new engineer, and they will understand the system's intended behavior.
The new method is to "prompt it out." The product manager’s instruction, buried in that chat log, was: "Let's make sure we give our most loyal customers a really nice thank you. Be more generous with them." What does "loyal" mean? How much is "more generous"? The answers are not documented in a table of business rules. They are encoded in the statistical weights of a model that has interpreted a vague,
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