AI coding tools can create a working prototype quickly, but local success does not prove software is safe to deploy. Generated code may contain placeholder authentication, broad database permissions, missing validation or assumptions that hold only on a developer machine. A production checklist creates a repeatable review path.
Plan the workflow
Make the environment reproducible: document runtime versions, dependencies, environment variables and startup commands. Commit lockfiles and run a clean CI build. Add unit tests for business rules, integration tests for database and API boundaries, and end-to-end tests for critical journeys.
Configure the controls
Test negative cases: unauthenticated requests, cross-tenant access, invalid payloads, duplicate submissions and expired sessions. Verify authorization on the server, not just hidden buttons or frontend route guards. Test the production build rather than only the development server.
Validate failure cases
Scan repositories and artifacts for secrets, keep credentials in a secret manager, validate uploads by content and size, and use least-privilege database roles. Run migrations against production-like data and check locks, destructive changes, long backfills and rollback limits.
Production checklist
Gate releases on tests, dependency checks, artifact scans, migration tests, staging smoke tests and documented rollback ownership. Related: AI-generated code security review.
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Architecture and prerequisites
Start by defining the production threat model and the data the application will handle. AI-generated code can include placeholder authentication, broad database access, insecure file uploads or permissive CORS because a prototype often optimizes for visible progress. Review the trust boundaries between browser, API, worker, database and third-party integrations. Identify which operations require authorization and where server-side validation occurs. A polished interface does not compensate for a missing permission check in the backend.
Configuration and implementation
Create a reproducible build from a clean checkout. Pin runtime and dependency versions, commit lockfiles and document environment variables without committing secret values. Run CI in an environment that resembles production and verify the actual production build. Remove development endpoints, debug routes, test accounts and sample credentials. Check that errors do not expose stack traces, SQL queries or internal service addresses to end users.
Security and compatibility
Test authentication and authorization independently. Verify that protected APIs reject anonymous requests and that a user cannot read or mutate another tenant’s records by changing an ID. Frontend route guards and hidden buttons are not security boundaries. Test session expiry, logout, password reset, role changes and account recovery. Use least-privilege database roles and ensure background workers cannot access unrelated customer data.
Testing and validation
Review database migrations with production-like data. Check for table locks, destructive column changes, large backfills and rollback limitations. During rolling deployments, old and new application versions may run at the same time, so use expand-and-contract patterns where necessary. Take a verified backup before a risky migration and test restoration. A migration that succeeds on an empty development database may lock a large production table for minutes or hours.
Monitoring and troubleshooting
Scan dependencies, source and build artifacts for known vulnerabilities and accidental secrets. Validate uploaded files by size and content, store them outside executable web roots and generate safe names. Apply rate limits to login, password reset and expensive API endpoints. Use security headers and secure cookie attributes appropriate to the deployment. Review outbound integrations and avoid trusting user-controlled URLs that could enable server-side request forgery.
Production operations
Add operational readiness before launch. Define health checks, structured logs, error tracking, backups, alert thresholds and an incident owner. Test a rollback from the release candidate and verify that the previous version can read the current database state. Run smoke tests after deployment and monitor real requests for latency, error rate and resource use. A production checklist should be a release gate with named owners, not a document that is completed after an incident.
Operational readiness and lifecycle
Before rollout, define an owner for the configuration, supported versions, dependencies and escalation path. Put configuration under version control and require review for production changes. Verify the implementation with a representative workload and record a baseline so future changes can be compared. Test a failure scenario, a restart and a rollback rather than relying only on a successful happy-path test. Monitoring should expose both the component’s internal health and the user-visible outcome, with alert thresholds that have a documented response. Keep a concise runbook containing diagnostic commands, expected signals, safe retry guidance and recovery steps. Review the design after the first production cycle and whenever an upstream dependency changes. Remove temporary exceptions, rotate credentials where applicable and update compatibility notes. This ongoing ownership is what turns a working configuration into a maintainable production service.
Scenario-based verification
For AI-generated applications, test cross-tenant access, authentication expiry, malicious uploads, injection payloads, dependency vulnerabilities and failed migrations. Review the generated code rather than trusting comments or tests supplied by the same generation process. Keep a human owner responsible for security sign-off and production rollback.
Performance and capacity validation
Define a measurable service-level baseline before enabling the change. Record normal and peak throughput, error rate, latency distribution, resource consumption and recovery time. A single successful test does not show how the system behaves under concurrency or after a dependency becomes unavailable. Run load tests that resemble real request patterns and include burst traffic, slow clients and large payloads. Compare results with the baseline and investigate regressions rather than accepting them because the deployment completed. Keep test data representative but non-sensitive, and repeat the test after significant runtime or dependency upgrades.
Logging and alert design
Operational logs should make failures diagnosable without exposing credentials or customer data. Include a correlation identifier, component version, action outcome and safe resource reference. Avoid logging bearer tokens, private keys, full database records or unrestricted request bodies. Define retention and access permissions for diagnostic data. Alert on repeated failures, queue growth, resource saturation and missed scheduled work, but ensure every alert has an owner and an action. Review alert noise after rollout and remove rules that do not lead to useful investigation.
Recovery documentation
Document the recovery path as carefully as the setup path. Include prerequisites, permissions, exact verification commands, expected outputs, rollback steps and conditions that require escalation. Test the procedure with someone who did not build the original system; this exposes assumptions that are obvious only to the author. Keep the runbook next to the configuration or source repository and update it when versions or workflows change. Schedule a periodic review so temporary exceptions, obsolete settings and unsupported dependencies do not become permanent parts of production.
Apply these checks to Vibe Coding Production Checklist: Add Tests, Migrations and Security Gates: retain the test evidence, identify the owner who accepts the production risk and define the conditions that trigger rollback. Re-run the checks after major upgrades or changes in traffic patterns.
