Ethical-AI-Syndicate
Publisher on askill
Use when evaluating AI capability test results against acceptance criteria for production deployment decision. Use after development complete and test results available. Produces structured go/no-go v...
Use when organizing and sharing prompts across teams. Use after prompts developed. Produces prompt registry, versioning system, quality ratings, and usage guidelines.
Use when deciding if a workflow step should be automated, AI-assisted, or left human-only. Use when stakeholders are enthusiastic about automation, when evaluating high-volume tasks, or when assessing...
Use when AI systems fail or behave unexpectedly. Use after incident detected. Produces incident classification, immediate response actions, rollback procedures, and post-mortem templates.
Use when evaluating whether a proposed AI capability is achievable with current LLM technology. Use before committing development resources, when stakeholders propose AI solutions, or when validating...
Use when documenting and prioritizing technical debt. Use after development identifies debt. Produces debt registry with impact scoring, remediation cost, and payoff timing recommendations.
Use when integrating AI with legacy enterprise systems. Use during solution design. Produces integration patterns, architecture recommendations, and migration strategies.
Use when deciding between custom AI development and vendor solutions. Use before project commitment. Produces structured analysis with scoring and recommendation.
Use when creating AI governance policies. Use when establishing AI program. Produces acceptable use policies, guidelines, and guardrails documentation.
Use when validating completed work against standards. Use before accepting stories as done. Produces DoD checklist evaluation, gap report, and completion recommendations.
Use when building grassroots AI adoption. Use during AI program scaling. Produces champion identification, enablement program, and community structure.
Use when balancing AI investments across initiatives. Use during planning cycles. Produces portfolio analysis, resource allocation, and investment recommendations.
Use when designing data annotation workflows. Use before model training. Produces annotation guidelines, quality control processes, and labeler training materials.
Use when analyzing competitor products and market positioning. Use before roadmap planning or strategy updates. Produces feature comparison, gap analysis, differentiation opportunities, and competitiv...
Use when specifying testable acceptance criteria for user stories. Use after user story drafted. Produces Given-When-Then scenarios, edge case coverage, and validation approach.
Use when designing APIs for AI services. Use during solution architecture. Produces API specifications, versioning strategy, and integration documentation.
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