Generative tools can accelerate analysis, implementation and test preparation. They do not change the properties of good production software: correct behavior, secure boundaries, maintainability, observable operation and explicit accountability. Speed without quality gates merely moves work into defects and risk.
Define requirements before generation
A model can implement an ambiguous requirement persuasively and incorrectly. Before code, every vertical workflow slice needs actors, inputs, rules, state transitions, failure paths and acceptance scenarios. Critical non-goals prevent a seemingly useful extension from changing scope or authorization unexpectedly.
Review architecture and data ownership
Generated code must not establish accidental system boundaries. Senior engineers review the data model, system of record, transactions, integration patterns and deployment. Decisions with long-term effect are documented.
Enforce several test layers
Unit tests protect isolated rules, integration tests cover databases and external contracts, and end-to-end tests protect critical user journeys. Characterization tests compare behavior with the legacy system. Tests must remain understandable and maintainable without the generating model.
A large number of generated tests is not evidence of quality if they simply repeat the implementation. Reviewers check whether boundary cases, authorization and negative paths are genuinely covered.
Integrate security into delivery
The NIST SSDF structures secure development around preparation, protection, production and response. In practice that includes threat analysis, least privilege, input validation, secret management, dependency scanning and a vulnerability process.
OWASP ASVS provides a verifiable application-security catalogue. The required level depends on data, threats and operating model; a blanket “secure” claim cannot replace that assessment.
Keep human accountability visible
Every change needs an accountable reviewer. Domain acceptance confirms behavior, engineering confirms technical quality, security assesses relevant risks and operations confirms readiness. AI can provide input but cannot assume any of those roles.
AI-accelerated engineering is therefore not a separate quality class. It is a productive method inside a normal, rigorous software lifecycle.