Practical guidance, design patterns, and templates to pilot AI assistants, agents, and automation workflows that save time while preserving reproducibility in research.
A practical, research-focused design checklist that explains scope, guardrails, provenance, validation, escalation, and security for AI agents. Includes concrete acceptance criteria, validation tests, common mistakes, and a concise design template you can copy into a project or an interactive checklist.
A practical, safety-focused library of reusable prompt patterns for literature review, experimental planning, code assistance, and data exploration — with expected outputs, validation checks, traceability practices, and a short governance checklist for when to require human review.
An expanded, practical brief that maps promising AI/automation capabilities for research, lists concrete candidate pilot experiments with objectives and quick success signals, identifies common blockers with mitigations, and provides a pilot-readiness checklist and prioritization guidance to help teams select feasible first bets.
A practical, stage-based playbook to assess, run, validate, and scale AI pilots in research settings. Includes milestone criteria, a complete pilot planning template (metrics, sample-size guidance), a risk & validation checklist, a roles & responsibilities matrix, and example retrospective and scale checklists. Recommended interactive forms and data-capture points are noted for easier reuse and reproducibility.
A practical, step-by-step framework to decide where AI adds value in research, validate model outputs, document model use in reproducible workflows, and manage risk throughout an AI-enabled research lifecycle.