The Mise en Place methodology uses contextual grounding, collaborative specification, and task decomposition to prepare AI agents for coding tasks, demonstrated in a hackathon where two hours of prep enabled rapid parallel development of a full-stack platform.
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Survey of 868 scientific programmers shows generative AI adoption is highest among the inexperienced, who prefer conversational tools, and perceived productivity correlates most with volume of accepted generated code rather than validation practices.
The Productivity-Reliability Paradox arises because AI code generators produce variable output while developers lack sufficient specification discipline, making governance models focused on specifications the binding constraint rather than model improvements.
citing papers explorer
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Mise en Place for Agentic Coding: Deliberate Preparation as Context Engineering Methodology
The Mise en Place methodology uses contextual grounding, collaborative specification, and task decomposition to prepare AI agents for coding tasks, demonstrated in a hackathon where two hours of prep enabled rapid parallel development of a full-stack platform.
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A survey of generative AI adoption and perceived productivity among scientists who program
Survey of 868 scientific programmers shows generative AI adoption is highest among the inexperienced, who prefer conversational tools, and perceived productivity correlates most with volume of accepted generated code rather than validation practices.
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The Productivity-Reliability Paradox: Specification-Driven Governance for AI-Augmented Software Development
The Productivity-Reliability Paradox arises because AI code generators produce variable output while developers lack sufficient specification discipline, making governance models focused on specifications the binding constraint rather than model improvements.