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Topics: Artificial Intelligence
When using AI systems, what are some best practices for ensuring the results you receive are accurate, relevant, and aligned with your original goals?
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Sarah Philbrick
PMI Team Member
Product Leader | AI Training Portfolio| PMI Asheville, NC, USA

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?

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Thaddeus Lin Il, USA
Effectively tailoring your prompt(s) with iterative improvements and refinement will lead to better quality of output. One will also need to validation and verification of the data that is used to generate the output for project management.
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Olive BUME ADA NGOLE Functional Manager| ENEO CAMEROON S.A Douala, Littoral Region, Cameroon
This is a very useful course as i have been training on how to form my questions. Getting to familiarised with the CREATE and RFT formates, i could clearly start forming my questions to match with the right prompt. The tool is new so providing precision to specific and clarity is critical and most often, i couldn't validate the responses i received.
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Ashley Villegas Marketing Project Manager Houston, USA
Jun 07, 2024 9:24 AM
Replying to Sergio Luis Conte
...
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.
Yes, I agree with the statement "predictive text on steroids". ChatGPT is now considered "old" and dependent on business structure an organization may or may not need to have additional human resources for a new business unit. At my previous organization the entire staff was challenged to conduct prompt engineering as it relates to individual departments as opposed to creating a new arm in the business. More experienced developers were responsible for model training.
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Sugat Ramteke PM III| Wilmar International Ltd. Thane, Maharashtra, India
We need to provide relevant context to the AI about the topic we are searching for. Providing examples and specifying the format of the output expected will help in getting precise response from AI.
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MUHAMMAD ASYRAF BIN KHALID TENAGA NASIONAL BERHAD Kuala Lumpur, Malaysia
Implement validation processes to ensure the accuracy and relevance of AI generated insights.
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SYED SAMEED BIN SHAKIL Project Manager| Hitachi Energy Karachi, Sd, Pakistan
I focus on providing precise information to AI so that it can create a results which gives clear view of projects, that can ultimately help us in fulfilling the requirements
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Ismail Elfatatry Planning Engineer| Projecs Cairo, C, Egypt
In order to ask for help, you need to make sure you can evaluate the help provided, especially if you're asking GenAI. Thus, expertise is vital in this side of the story to avoid misleading results. Insights can be multi-directional. Thus, it may be true, just not for you! Beware of all the biases out there and continue to prosper!
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Victor Cristobal Morocho Moreno Quito, Pichincha, Ecuador
Writing the prompt following the suggestions above is an appropriate way to obtain expected and useful results. Additionally, it is relevant that these results are also validated by a knower or an expert in the area where such data or results will be applied and used.

Escribir el prompt siguiendo las sugerencias expuestas es una vía adecuada para obtener resultados esperados y útiles. Adicionalmente, es pertinente que esos resultados sean también validados por un conocedor o un experto en el área donde esos datos o resultados serán aplicados y utilizados.
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Syed Salman Ahmed Ontario, Canada
As an experienced with IT Projects, my approach to managing AI projects includes the following
1. Defining Clear Objectives and Criteria for Success
2. Conducting Thorough Testing and Validation
3. Regularly Monitoring Performance
4. Using Explainable AI Techniques
5. Continuous Refinement and Updating
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USMAN OLUSEGUN Lagos, LA, Nigeria
I think it's crucial to establish clear criteria for outputs, implement robust testing protocols, and continuously refine models based on feedback. Regular validation against real-world scenarios can help ensure results are accurate and relevant. These best practices will help align AI outcomes with our original goals effectively.
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