Why this decision matters
AI quality control should cover requirements, data, design, implementation, integration, testing, deployment and handover rather than being postponed until the end.
A useful process connects each stage through versions, reviews, tests and change records so requirements, data, models, code and environments remain traceable.
Conditions to confirm before development
- Baseline objectives, boundaries and acceptance definitions
- Record data sources, versions, assumptions and technical risks
- Use code review, automated checks and interface integration tests
- Retest on target systems, close issues and complete handover
Implementation and delivery approach
Set review gates according to risk: data and metrics for algorithms, interfaces and regression for software, target-device stability for embedded work.
Evidence includes requirement baselines, data inventories, design decisions, versions, test reports, issue records, deployment logs and acceptance checklists.
Acceptance boundary
Process depth should match project size. Document volume is not a substitute for engineering evidence, and unverified items must remain clearly marked.