Director, Learning Design & Development| PMIAsheville, NC, United States
Validating and checking outputs is critical when working with AI systems like Generative AI. Such validation approaches may include establishing clear criteria, implementing strong testing protocols, and continuous refinement.
In your experience with AI, what are some best practices for ensuring the results you receive are accurate, relevant, and aligned with your original goals?
Well, in response I am being transformed from an Electrical engineer to IT project Manager in my journey so far I have tried most stupid questions and later i learned that AI can do great things in supporting my daily tasks when we make AI to learn more on our working style and expectations by giving continuous feedback after every response and iterative questions to get a good final outcome which can be reviewed and adjusted for our work and also later feed the adjusted document to AI for it to learn and adopt for our convenience.
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Anonymous
Engaging with the LLM as if you were having a conversation. Be specific in the information you provide using the CREATE formula and understand that the first answer may not be the best one. Continue the conversation using iterative prompt refinements.
AI is a broader term. Generative AI is just an ancient model but everything "explode" when Google published the new architecture called transformer in 2017. So, with that said, take into account that generative AI is just "predictive test with steroids" just simplifying the model. With that said, two key points has to be taking into account when somebody works with AI: 1-human in the loop. 2-AI without Data (today called data science discipline or big data or whatever) is the same thing that live without oxygen. Talking about generative AI all related to technology has almost not impact with relation to all related to non-technological roles and activities. What you stated about accuracy and things like that are easy to implement because there are a lot inside disciplines like statistics. Most of them to make things "a priori" to prevent instead of cure. Few organizations taking into account that when generative AI environments are put in place almost a new business unit has to be created where roles like lawyers, linguistic, diversity and inclusion specialist must be hire to help on put it in place.
Good explanation !
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Antonio FonsecaProject Manager| ARES Aeroespacial e Defesa SARio De Janeiro, Rio De Janeiro, Brazil
Human-on-the-loop Validation and Evaluation. It's a good practice to keep your finalized respond from a LLM to be evaluated by another LLM asking to bring back LLM's opinion, advantages and disadvantages, and opportunities to adjust ro enhance the respond.
ROHIT SEAM Global Head of Indirect R&D Sourcing and Procurement| Applied Materials Inc.Santa Clara , CA, United States
h2This is what i have learned as a new beginner of prompts that have worked for me. Start by clearly stating the outcome you want, the audience, and a few success criteria so the AI stays aligned to your goals. Add constraints that prevent guessing, and require it to list assumptions, risks, and a confidence level so you can see where it’s making leaps. When facts matter, ask for traceability (citations or “use only what I provided”) and do a quick second-pass critique or red-team to catch gaps. Finally, validate key claims with lightweight spot-checks and reuse a standard prompt template for repeatable quality./h2 Saving Changes...
A project manager cannot rely on AI outputs with 100% confidence. Effective use of AI requires the PM to possess sufficient domain knowledge and a clear understanding of the desired outcomes in order to evaluate and validate the results. If a PM lacks subject-matter understanding, they cannot effectively act as an evaluator to determine whether the AI-generated output is accurate, precise, and aligned with project objectives, organizational strategy, and stakeholder expectations. The PM must therefore maintain strong contextual and technical awareness, critically review AI-generated content, and re-validate assumptions, constraints, and deliverables. Where misalignment or gaps are identified, the PM should iteratively refine the prompts, provide clearer requirements, and incorporate relevant examples, scenarios, or acceptance criteria to guide the AI toward the intended outcomes. In this sense, AI should be treated as a decision-support and productivity tool—not a replacement for professional judgment, accountability, and governance as defined by PMI best practices. Saving Changes...
Given instructions in prompt should be Simple, Clear, Precise, Not Vague/unstructured, avoid Jumble prompt. Use CREATE formula and Refine prompts using (Chain of Thought/Feedback, Tree of Thought, Persona Pattern & ReAct pattern.
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Magdy FahmyProjects Manager| Al HABASHI GENERAL CONTRACTINGCairo, Egypt
To get the best from AI, tell it exactly who to be and give it all the facts first. Be specific about what you want—like a table or a short list—and ask it to think "step-by-step" to avoid mistakes. Finally, treat the first answer as a draft that you must double-check and refine until it's perfect.
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