Pattern #2 in the Four Patterns of AI Native Development series. The better AI becomes, the less we need to focus on the implementation and can work on describing the intent. This shift can happen through chat conversations or product requirement documents — creating a central collaborative space.
The evolution
Development has progressively abstracted away from low-level operations. Where programmers once manipulated switches and machine code, they now use natural language prompts. The LLM, like a compiler, translates our thoughts into code.
Shared prompt libraries
Developers can save reusable prompt snippets — configuration patterns for project structure, tech stacks, testing approaches, and API specifications. These shareable libraries prevent repetition across teams and organizations, paralleling how developers have traditionally used configuration files.
Specification as agreement
Shared libraries enable conversations between stakeholders. Modern development must address both building things correctly and building the right things — creating artifacts like PRDs, architectural diagrams, and acceptance criteria.
Tests as safeguards
Executable tests confirm specifications and catch LLM hallucinations. They uncover ambiguities in natural language requirements and maintain consistency as applications evolve.
Intent over implementation
The crucial shift demands prioritizing goals and desired behaviors over granular mechanics. This requires deep understanding of the problem while avoiding getting bogged down in the details. The collaboration between human creativity and AI capability produces more innovative solutions.
Full article at tessl.io. Part of the Four Patterns of AI Native Development series. Originally posted on LinkedIn.