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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
Director, Learning Design & Development| PMI Asheville, 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?

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Usman Rashid EME| Defense Industry Karachi, Pakistan
Some of the strategies that can be used are "establish verification system", "deploy automate systems" and "conduct controlled experiments". Moreover, if input provided using strategies like "CREATE" will result into better- and high-quality results from AI.
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Inumidun Oluwatayo United States
Some best practices for ensuring the results we receive are accurate, relevant, and aligned with your original goals when using AI include:
- Breaking down complex tasks into small er, manageable ones
- Inputting a persona
- Being specific about what exactly we want
- Refining out questions
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Anonymous
The better context and structure way of asking is meaningful; now I realize that without knowing, I have been feeding some relevant information, context, situation, examples, and pertinent information, and so far, it has worked really well.
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Janelle Alexander Heritage Petroleum Company Limited San Fernando, Sfo, Trinidad and Tobago
In my experience using AI, the following has worked best for me:
1. Provide the context
2. Be clear and specific
3. Provide the outcome and format expected
4. If needed, reword the prompt
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Anonymous
I think that the key element is to be specific and interact to refine the results. In addition, I also think that it is important to ensure that you are analyzing and validating the information provided before using the outcome.
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Bingye Yu Branson, Mo, United States
Iteration, "ReAct", keep trying & "testing" the prompts. I like the 3rd AI PMI course, talking about the adaptability and dynamic prompts.
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Takunda Dzimbanhete Masvingo, MV, Zimbabwe

Asking the LLM to critique its response using a different relevant persona is good way to another angle to the response and tailor it to a particular audience.



For example, when working on a progress report for a diverse audience, you might want to ensure that the report will address the social concerns of a particular stakeholder. You can ask LLM after generating the report as follows:



"You are a social and corporate responsibility manager on a slimes dam project in Zimbabwe. Your role on the project is to ensure that each month the project maintains the value it promised the community i.e. job creation, dust suppression to minimize air pollution and conservation of the environment. I want you to critique this report and ensure it addresses your concerns clearly. Give me suggestions to improve the report.''



By doing this you ensure that the report speaks to each of its audience and therefore delivers value.

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Ulises Pontes Garcia Queretaro, QUE, Mexico
We must learn to give better prompts to artificial intelligence: be clear, avoid ambiguities, and become familiar with its use. Once we obtain the results, it is important to review them from beginning to end and make the corrections that our experience suggests.
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Anonymous
AI gets you on the right path but not the destination. I think prompting the tool to start generating ideas is incredibly helpful, but the expertise project teams bring to pushing things forward is critical.
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Anant Verma Mississauga, Canada
Responses can be made more relevant to the gaols with the following steps:
1. Initiate prompt
2. Check the output
3. Refine prompt
4. Validate the output
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