Evidence before confidence
Every generated claim should be traceable to approved evidence, with explicit uncertainty when support is missing or conflicting.
ENGINEERING / STANDARDS
The controls around the model matter more than the model alone.
A practical standard for building GenAI systems that can be evaluated, explained, secured, and operated responsibly.
Deterministic controls at risk boundaries
Authorization, PII handling, schemas, policy checks, and approval gates belong in code—not in model instructions alone.
Evaluation before release
Retrieval, faithfulness, safety, latency, cost, and regression slices are measured independently before promotion.
Bounded autonomy
Workflows expose state, permissions, retries, idempotency, and recovery paths before they earn broader tool access.
Privacy-safe observability
Traces capture decisions and provenance while redacting prompts, sensitive payloads, and unnecessary personal data.
Public-safe communication
Case studies distinguish verified outcomes from synthetic demonstrations and never disclose confidential implementation details.
OPERATING BOUNDARY
Make the evidence boundary visible.
For every system, I want to answer five questions: what data was allowed, what path ran, which controls fired, what evidence supported the result, and how the team recovers when the answer is wrong.