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Lewis, P., Perez, E., Piktus, A., Petroni, F., Karpukhin, V., Goyal, N., Küttler, H., Lewis, M., Yih, W. T., Rocktäschel, T., Riedel, S., & Kiela, D. (2020). Retrieval-Augmented Generation for Knowledge-Intensive NLP Tasks. arXiv: 2005.11401.

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• TITLE: Team‑Level Guide for Prompting, Governance, and Value Delivery

  AUTHORS: Armando Herrera

  KEYWORDS: Project Governance, PMBOK® Guide Principles, Prompting, AI Risk Management, Benefits Realization, ISO/IEC 42001, PMI Code of Ethics

  JOURNAL NAME: Open Journal of Business and Management, Vol.14 No.2, March 2, 2026

  ABSTRACT: Artificial intelligence (AI), especially generative AI, has moved from pilot to production across industries, reshaping project delivery from planning to execution. Adoption is rapid, yet outcomes remain uneven: many teams report frustration when outputs sound plausible but lack accuracy or context. This guide addresses that gap by combining evidence-based practices with PMI-aligned governance. It explains how large language models (LLMs) work (probabilistic next-token prediction), why hallucinations occur, and how to mitigate them through structured prompting and risk-based controls. It defines key terms—complicated vs. complex tasks, hallucination, and sets a clear boundary: AI accelerates well-defined, low-risk work; humans retain accountability for complex, high-consequence decisions. Practical tools include the CAPTURES™ prompt pattern, a 90-day rollout plan and lightweight controls mapped to ISO/IEC 42001. The goal is to deliver measurable cycle-time and clarity gains while maintaining trust and ethical standards.