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Cross-cutting

Patterns that apply in every field we cover: how to frame a prompt, how to verify AI output, and how to save a workflow so it is repeatable.

Patterns

  • Prompt → Verify → Store: draft with AI, verify against source, save the winning prompt to your workflow library.
  • Evaluate before you trust: run any new AI workflow on a known input first; never let the first output be the production output.
  • Attribution and sourcing: any claim about tools or model behavior gets a source link.

Cross-cutting topics

The taxonomy defines an explicit cross-cutting dimension (ai.* topics, added in taxonomy v1.1) that applies to every field: prompting, agents, automation, evaluation, safety, adoption, data, documents, reporting, vendors, and security. See the content taxonomy and research/taxonomy/taxonomy-v1.1.json.

Playbook template (with PAR-4)

The canonical playbook structure described in the sample playbook is the contract every field playbook follows.

Planned topics

  • The PartlyGood prompt framework
  • Evaluating an AI tool for your workflow
  • Building your personal AI workflow library