Advanced AI Engineering Checklist

Use This Checklist Before Shipping

Advanced AI engineering requires more than a working demo. Before shipping, teams should verify architecture, data flow, retrieval quality, tool access, evaluation, observability, security, and operational readiness.

The Checklist

• Task boundary: Is the system role clearly defined?
• Inputs: Are user input, retrieved content, app state, and tool outputs separated?
• Retrieval: Are chunking, metadata, permissions, freshness, and evals working?
• Outputs: Are schemas, validation, citations, and fallback behavior defined?
• Tools: Are permissions, approval gates, idempotency, and logging in place?
• Security: Are prompt injection, data exfiltration, secrets, and tenant isolation addressed?
• Evals: Are golden datasets, regression tests, safety tests, and release gates in place?
• Observability: Are prompts, retrieval, tool calls, latency, cost, errors, and feedback logged?
• Deployment: Are staged rollout, rollback, fallback, and incident response plans ready?

The Rule

If the AI system can access sensitive data, call tools, or affect users, it needs deterministic controls outside the model.