What These Field Guides Are
- Playbooks for designing, governing, and operating real AI systems
- Written from system failure, audit, and deployment realities — not theory
- Built to expose gaps before incidents, audits, or scale
Who Uses Them
- Engineering teams building or maintaining AI systems
- Business and product leaders deciding what should (and should not) be automated
- Governance, risk, legal, and policy teams responsible for accountability
How They’re Used
- To align teams on what “safe to ship” actually means
- To evaluate readiness before scaling or procurement
- To support internal reviews, audits, and executive decisions
How They Fit Together
- Field guides define what good looks like
- Questionnaires measure where you are
- TrustEval verifies how systems behave in practice
Practical playbooks for building, governing, and adopting AI safely.
The guides explain what good looks like, the questionnaires measure where you are, and TrustEval verifies how systems actually behave.
These Guides are practical references for building, governing, and deploying AI systems responsibly. They translate abstract AI risk, policy, and engineering concepts into concrete system design, operating practices, and decision frameworks.
Each guide is written for a specific audience—engineering, business, governance, or technical leadership—and can be used independently or together. Teams use them to align on expectations, identify gaps, and establish shared language across roles before systems are built, scaled, or audited.
The field guides are designed to work alongside Oyez questionnaires and TrustEval: the guides explain what good looks like, the questionnaires measure where you are, and TrustEval verifies how systems actually behave.
Related Questionnaires
These questionnaires are designed to be used alongside this field guide. Each one tests whether the concepts discussed here exist in practice — across strategy, intake, engineering, governance, and operations.
Strategy & Alignment
Use-Case Intake & Risk Decisions
Build, Reliability & Grounding
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AI Engineering Maturity (Systems)Assesses whether AI systems are production-ready, including evaluation, monitoring, drift handling, and operational reliability.
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RAG & Grounding ReadinessUsed by teams building retrieval-augmented systems to assess evidence handling, source control, grounding quality, and traceability.
Governance, Procurement & Oversight
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AI Governance & Compliance ReadinessUsed by legal, risk, compliance, and policy teams to assess enforceable controls, auditability, and accountability structures.
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AI Vendor Procurement & Due DiligenceUsed before purchasing or renewing AI tools to assess evidence access, failure transparency, exportability, and vendor risk posture.
Operations & Incident Review
These questionnaires are most effective when completed by multiple roles (e.g., executive, engineering, governance) and compared for alignment gaps.