As senior PMs, start by scaffolding every AI interaction with RTF and CREATE. Explicitly set the Role (e.g., risk analyst), bound the Task (in/out of scope), and demand a precise Format (sections, tables, JSON). Provide Context: objectives, constraints, stakeholders, timelines, and Definition of Done aligned to your original goal. Make a clear Request and specify Tone (exec brief vs deep dive) and Evaluation criteria: acceptance tests, required sources, and risk thresholds. Require the model to surface assumptions, data gaps, confidence levels, and to ask clarifying questions before proceeding.
To ensure accuracy and relevance, ground the AI on authoritative references: policies, contracts, specs, metrics—prefer retrieval over open‑web. Instruct it to produce options with pros/cons, impacts, reversibility, and monitoring signals, not just a single answer. Demand source‑backed claims with quotations and links, plus “last updated” dates and regional applicability notes. Run a second‑pass verification: self‑check, cross‑model comparison, or targeted spot‑checks of calculations and logic. Add a pre‑mortem: what could be wrong, what evidence would change the recommendation, and how to test fast.
Wrap this in governance and iteration. Protect data (no sensitive info; approved tools only), version your prompts, and keep decision records and audit trails. Establish a review cadence with a scoring rubric (accuracy, relevance, completeness, compliance) and iterate until thresholds are met; automate commodity outputs to free time for stakeholder alignment and risk burn‑down. Monitor model and source drift, retrain prompts as contexts evolve, and log deviations with corrective actions. Close the loop by mapping outputs to owners, dates, and measurable outcomes so the AI remains aligned to the original goals.
PMI Team Member
