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Specification Engineering - mklabgr - 08-15-2026
Specification Engineering
Summary
The KDnuggets article argues that “specification engineering” is emerging as the next important skill beyond prompt engineering as AI systems evolve from simple chatbots into coding agents, research assistants, data-science copilots, and autonomous workflows. While prompt engineering focuses on how to ask an AI for something, specification engineering focuses on clearly defining what a correct result means: the objective, context, inputs, constraints, expected output, edge cases, evaluation criteria, verification tests, and possible failure modes. This becomes especially important when AI writes code, analyzes datasets, produces structured outputs, or performs multi-step tasks, because a plausible-looking response may still violate important requirements.
Instead of the traditional workflow of prompt → output → manual correction, the article proposes specification → generation → validation → revision → audit, where AI outputs are systematically checked against explicit requirements. The central message is that prompt engineering is not disappearing but becoming part of a broader engineering discipline: as AI becomes increasingly autonomous, the valuable skill will be the ability to design precise, testable, and verifiable tasks so that the AI, user, and evaluator share the same definition of “done correctly.”
ARTICLE