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?
A very good question and also difficult to answer as well. However you have to go to the basics and say as far as you are concerned, how well are you versed with the subject at hand ?. There are facts which the AI will generate and if you can verify these facts the more reliable the generated response will be. The fewer the facts then it means that the Generative AI response is far from meeting your original goals. Then it becomes very critical that you review the accuracy , relevancy and the alignment of the response to your original need. Unfortunately there are no clearly defined metrics that one can use a model to evaluate an AI generated response. So from my personal experience I basically restrict AI to an area where i have sound knowledge of , else it becomes almost impossible to verify details generated by an AI if you venture into unchartered territory. However with long usage and exposure your confidence also tend to increase as well. The best practice and protocol to follow would be to consult subject matter expects to validate the AI generated response before making critical decisions based on it to avoid any inherent associated risks which you might be not aware of.
I totally agree with this, you have to be versed with the subject matter, then you can verify the information that the AI model has provided for you Saving Changes...
Before prompt engineering, I usually spend more time sending requests to the AI and keep refining it for me to reach my desired output. But now with the CREATE formula, I get almost all of what I need with just one click. The time I spent sending in requests has now been reduced to half, where I will use that half period to build my prompts. However, what I am most excited about is not just the exposure to the capabilities of the AI models, but the "HUMAN IN THE LOOP" concept, which seems very lacking in most places where AI tools are being utilized. Sometimes, when I receive correspondence from other institutions, without even running it through any detection platform, I can tell that it was AI-generated, given the generality of its nature. Therefore, it's a concept that is very important to follow. That being said, I will proudly say that with my little knowledge of prompts and their engineering through this course, I will champion a small training for my colleagues at work, so that they can also utilize AI tools, which will massively boost our project team's efficiency.
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1 reply by MAMAA ENYIMAH ACKAH
Nov 02, 2025 7:12 AM
MAMAA ENYIMAH ACKAH
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I was also facing the same challenges and often spent a lot of time trying to get AI to understand my prompts. I really like how the CREATE framework helps you be more specific and detailed with your requests. I’d like to add that AI serves as a great support tool for project managers, but it’s important to do some research and reading around what you need support with. That way, you can assess whether the responses you receive are accurate and relevant. Sometimes AI can provide inaccurate or inconsistent information, but if you’re actively engaged with the work, you can easily catch those irregularities. It’s still a growing technology, so a bit of human validation goes a long way.
Before prompt engineering, I usually spend more time sending requests to the AI and keep refining it for me to reach my desired output. But now with the CREATE formula, I get almost all of what I need with just one click. The time I spent sending in requests has now been reduced to half, where I will use that half period to build my prompts. However, what I am most excited about is not just the exposure to the capabilities of the AI models, but the "HUMAN IN THE LOOP" concept, which seems very lacking in most places where AI tools are being utilized. Sometimes, when I receive correspondence from other institutions, without even running it through any detection platform, I can tell that it was AI-generated, given the generality of its nature. Therefore, it's a concept that is very important to follow. That being said, I will proudly say that with my little knowledge of prompts and their engineering through this course, I will champion a small training for my colleagues at work, so that they can also utilize AI tools, which will massively boost our project team's efficiency.
I was also facing the same challenges and often spent a lot of time trying to get AI to understand my prompts. I really like how the CREATE framework helps you be more specific and detailed with your requests. I’d like to add that AI serves as a great support tool for project managers, but it’s important to do some research and reading around what you need support with. That way, you can assess whether the responses you receive are accurate and relevant. Sometimes AI can provide inaccurate or inconsistent information, but if you’re actively engaged with the work, you can easily catch those irregularities. It’s still a growing technology, so a bit of human validation goes a long way. Saving Changes...
Validating AI-generated outputs is essential to ensure accuracy, with the human touch ultimately serving as the final decision-maker. Saving Changes...
Mohamed Ahmed ShabanSenior Project Management| Hassan Al-Syed CounsultaionSaudi Arabia, K.S.A, 3, Saudi Arabia
We need Best Practices for Ensuring AI Results Are Accurate, Relevant, and Aligned with Goals
Define Clear Objectives Before PromptingStart with a well-defined goal for what you want the AI to deliver.Use prompts that include context, constraints, and success criteria.Validate Against Trusted SourcesCross-check AI outputs with authoritative data or subject matter experts.Never assume AI responses are fully accurate without verification.Iterate and Refine PromptsUse iterative prompting to clarify ambiguous outputs.Apply advanced patterns like Chain-of-Thought or ReAct for complex reasoning.Set Boundaries for Compliance and EthicsEnsure outputs align with organizational policies, legal requirements, and ethical standards.Include compliance checks in your workflow.Monitor for Bias and RelevanceReview outputs for bias or irrelevant information.Adjust prompts to emphasize neutrality and relevance to project objectives.Use Human-in-the-Loop OversightKeep human review in critical decision-making steps.AI should augment, not replace, strategic judgment.
Treat AI as a powerful assistant, not an autonomous decision-maker. The PM’s role is to guide, validate, and integrate AI outputs into the broader business context.
Saving Changes...
JULIO GOMEZPM Consultant| MOMO INGENIERIA LTDA.Santiago, Santiago, Chile
The PM's experience is fundamental to validating the results. Saving Changes...
Abedalaziz SederRegional Digital Commerce Leader| IKEAJeddah, Saudi Arabia
By making sure the data is clean, asking specific questions, and reviewing the output. All of this will depend on the AI tools we are using and our needs for using them. Saving Changes...
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