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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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Sanjay Srivastava PM Consultant| Kyndryl Solutions Pvt. Ltd
In my case I usually ask AI to present its output or solution in a logical diagram way too. It helps me to simulate the scenario in my mind like the situation evolving and have my own checkpoint to validate in between. Sometimes I also put the same request to other AI tool, just to watch/compare like two different doctors opinion. That's the way I use AI as my helper, and not as my boss :)
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Diego Diez Loaiza Medellín, ANT, Colombia
In my experience, instructions work “very well” when I am as specific as possible, provide context, know the audience, and, importantly in my case, give an example. In addition, I always check the result, test it, and redefine it if the situation warrants it. To conclude all under the framework engineering formulas.
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Anonymous
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Anonymous
Set Clear Goals – Define what you want to achieve with specific context and outcomes.
Craft Precise Prompts – Use detailed, context-rich questions to guide the AI.
Verify Information – Cross-check AI responses with trusted sources, especially for critical topics.
Iterate and Refine – Improve results by rephrasing or asking follow-up questions.
Know the Limitations – Be aware that AI can generate incorrect or outdated info.
Use AI as a Collaborator – Combine AI output with your own expertise and judgment.
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stalin lucena Venezuela (Bolivarian Republic o
Start with a detailed frame. "CREATE" is a good approach. Get the answer. Refine (Iterate).
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Natanael Riahi PM Consultant| State Street Bank Les Lilas, France
Be specific in the request, add as much context as you can, and check the quality of the results and the sources provided.
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Natanael Riahi PM Consultant| State Street Bank Les Lilas, France
Be specific in the request, add as much context as you can, and check the quality of the results and the sources provided.
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Anonymous
Be specific and clear with your prompting, refine and iterate as you go, ask for more specific responses, provide additional data to augment the LLM.
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Catherine Kha
Jun 11, 2024 2:25 PM
Replying to Melissa Stockbridge
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Some of my items may be redundant but the most important things in my experience so far is:

Be precise and clear.
Be sure you explain jargon or specialized terminology
Provide the context for all of your requests
Be sure you provide the outcomes you are expecting
Experiment and refine as you go

I've found breaking down big problems can be better refined by chunking the whole into natural sections and working to refine each section and then working to put them back together.
I agree with your points. It is crucial to elaborate on specialized terminology and industry-specific context so that the outputs are even relevant. And for me sometimes, it can take too much time and effort to keep refining the input data.
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Alvaro Corral Naveda The University of Winnipeg Winnipeg, MANITOBA, Canada
Jun 11, 2024 11:22 AM
Replying to Omar Jabbar
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I don't disagree with the answers above, but I keep it very simple. Make sure your data is clean, ask specific questions, and review the outcome. All of this will depend on the AI tools you are using and your needs for using them. Once you have this figured out, you will be good to go.
Continuing review and improvement are essential in this case.
I hope that helps.
Regards,
Definetly agree! I have read very useful comments, but yours is very assertive "keep it simple".
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