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AAOE
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Specification-Driven Development

Observation

During the SolOh ERP Modernization, AI implementation quality varied greatly when requirements were incomplete. Once specifications became complete and stable, implementation quality became predictable. As specifications matured, the implementation prompt itself became relatively unimportant compared to the specification behind it.

Context

These observations occurred during the SolOh ERP Modernization, most concretely on the Authentication vertical slice, where the project shifted from limited, incomplete specification toward specification-first feature development. CS-0001 — SolOh ERP Modernization is the evidence source for this Discovery.

Evidence

  • Complete specifications consistently produced higher-quality implementations than incomplete ones.
  • Stable requirements enabled predictable AI implementation.
  • Shared specifications reduced ambiguity between specialized AI agents working on related parts of the same feature.
  • Implementation prompts became significantly simpler once specifications were complete.

Discovery

Engineering quality correlates more strongly with specification quality than with prompt quality. The primary engineering asset is therefore the specification rather than the implementation prompt.

Why It Matters

This observation appears generally applicable beyond SolOh:

  • Repeatability — a complete specification produces consistent results across implementation attempts, while a prompt's wording does not.
  • Traceability — a specification can be referenced and connected to the decisions behind it; a prompt is typically transient and undocumented.
  • Specialization — multiple agents can work from the same specification without needing to share the same prompt history or context.
  • Organizational knowledge — a specification can outlive the task that produced it and inform future work; a prompt generally cannot.

Consequences

  • Specifications become long-lived engineering assets.
  • Prompts become transient execution artifacts.
  • AI agents collaborate through shared specifications rather than shared prompts.
  • Engineering organizations benefit from investing in specification quality.

Case Studies

This Discovery is referenced by, and later generalized into, the following AAOE specifications:

Future Evolution

Future Case Studies may validate, refine, or challenge this Discovery. Engineering Discoveries evolve through additional evidence, not through revision of this record's original observation.