Clear guidance on AI-assisted development, difficult codebases, product delivery, and maintainable engineering.
Why fast AI-generated prototypes become difficult to own—and the technical checkpoints that keep them useful, secure, and maintainable.
A practical checkpoint for ownership, data, security, testing, monitoring, dependencies, releases, and recovery before real users depend on an AI-built product.
A practical way to establish ownership, reproduce the system, trace critical workflows, rank risks, and decide what to preserve before committing to delivery.
Compare risk, evidence, replaceable boundaries, operating constraints, and migration cost before committing an unstable product to refactoring or a rewrite.
Separate acceptable learning shortcuts from debt that threatens security, data, releases, observability, cost, and the ability to change the product safely.
Choose a delivery model by ownership, coordination, scope uncertainty, internal capacity, and acceptance—not by whichever contract label sounds most flexible.
Prepare outcomes, decision owners, controlled access, reproducible evidence, acceptance criteria, and a working agreement before delivery begins.
Assess outcomes, data, failure consequences, permissions, evaluation, human review, fallback, and monitoring before connecting AI to a real workflow.
Turn a successful demo into an operable service with representative evaluation, security boundaries, human controls, monitoring, rollback, and staged release.
