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Extension & Schema Evolution

AI Products must support extension and schema evolution — enabling adaptation to new domains, requirements, and integration contexts.
Without extensibility, AI Products become brittle, forcing costly rewrites instead of sustainable growth.

Why Extension & Schema Evolution Matter​

  • Adaptability → Allows products to evolve with new tasks, formats, or regulations.
  • Reusability → Extensible design ensures broader applicability across domains.
  • Governance → Extensions must remain traceable and compliant.
  • Innovation → Community or organizational extensions enable rapid experimentation.
  • Future-Proofing → Products survive paradigm shifts without breaking compatibility.

Extension Mechanisms​

AI Products should declare how they may be extended:

  1. Schema Evolution

    • Backward-compatible updates to input/output schemas.
    • Deprecation paths for breaking changes.
    • Use of Semantic Versioning to manage schema evolution.

  2. Plug-In or Adapter Model

    • Support for additional modules (e.g., domain-specific classifiers).
    • Integration of third-party or community plug-ins.

  3. Custom Metadata Fields

    • Optional extension slots in metadata (JSON-LD contexts, RDF vocabularies).
    • Controlled vocabulary to avoid semantic drift.

  4. Composable Extensions

    • New capabilities can be added by chaining with other AI Products (see Composability).

Governance Requirements​

  • All extensions must declare:

    • Ownership (who developed the extension).
    • Compatibility (what versions they support).
    • Compliance (alignment with regulations and policies).

  • Extensions must not introduce shadow features that bypass declared Prohibited Uses.

Example​

AI Product: Enterprise Sentiment Analyzer

  • Schema Evolution: v1.2 adds support for multilingual inputs; backward-compatible with v1.1.
  • Plug-Ins: Domain-specific sentiment lexicons (finance, healthcare) can be added as modules.
  • Custom Metadata: Extension slot for organization-specific risk classifications.
  • Composable Extensions: Chained with a summarizer product to provide "sentiment summaries."

Summary​

  • Extension and schema evolution enable AI Products to remain flexible, relevant, and future-proof.
  • Mechanisms include schema evolution, plug-ins, custom metadata, and composable chaining.
  • Governance ensures extensions remain traceable, compliant, and aligned with product intent.

Principle: An AI Product without extensibility is fragile — it breaks under change rather than adapting to it.