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In your experience with GenAI, how has refining a prompt drastically changed the output quality?

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Sarah Philbrick
PMI Team Member
Director, Learning Design & Development| PMI Asheville, NC, United States

With Generative AI, iteratively refining and optimizing prompts can lead to better AI-generated results. This may involve adjusting the specificity or clarity of the prompt to increase relevance and accuracy of results.

What examples do you have of how improving a prompt drastically changed the output quality?  What specific changes did you make that led to the improvement?

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The best result to have from AI is through refining. The more you refine, better outputs you get and better yourself you get at refining.
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1 reply by Allison Yenchik
Jan 17, 2025 1:08 PM
Allison Yenchik
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Completely agree with this!
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Davide La Valle Digital Business Partner| IntesaSanpaolo Turin, Italy
After many hours of learning and training, I found a balance between a simple prompt that can immediately lead to a good-enough result, and a detailed and specific prompt that can minimize hallucinations and let me get the specific result that I'm looking for. Many times this process requires a few steps of refinement that can define a specific goal that meet my needs, and giving context, examples of output and specific requirements can boost the quality of output.
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Sikandar Hayat Islamabad, Pakistan
In my experience with GenAI, refining a prompt can significantly enhance output quality by providing clearer context, reducing ambiguity, and guiding the AI towards more precise and relevant responses. A well-crafted prompt helps the AI understand the specific requirements.
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Renzo Morante Wake Forest, Nc, United States
Refining has been key !! You will never get the perfect answer on the first prompt. You need to continue adding context and being more specific in order to get more accurate and meaningful results.
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Rich Weller MBA, PMP, Project Certified, Agile Certified, AI Credentials| MIGSO-PCUBED Milan, Mi, United States
When working with the GenAI tools, it is very similar as to working with an individual. Sometimes my instructions may not be clear and as a result, I don't get the response that I would expect. Keeping the "human in the loop", it is up to me to revise my request (prompt) to get a better result.
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Timothy McIntyre Project Consultant| 90 Degree Benefits Helena, Al, United States
Simply refining the prompt by adding in more details regarding what you need makes all the difference. Just be careful not to share any proprietary data or information in your prompts. The key is to keep the data you enter generic while being creative in your questions for the AI tool to get a specific response/tool/template.
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Rory Miott Program Manager| Freddie Mac Potomac Falls, Va, United States
It mimics working with a human, the better context and instruction the better the outcome.Starting with your initial request you can see components that are the most valuable and require refinement and detail
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Prashanth Shankhawaram PM I| Privately Placed Company Bangalore, Karnataka, India

I always start with the end in the mind as to what outcome I want to achieve.



I start with the problem statement, actor that I play (this sets the context), I add specific additional context, examples such that the model understands what I am looking for.



I emphasize aspects that I want the model to treat priority.

I repeat this until the point that I get a close match and satisfied response.

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Hakam Madi Independent Consultant Amman, Jo, Jordan
Jun 23, 2024 5:12 PM
Replying to Booma Pugazhenthi
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I disagree with the idea that frameworks like R-T-F, T-A-G, B-A-B, C-A-R-E, and R-I-S-E are essential for prompt creation. These structures can be restrictive and may stifle creativity and flexibility in designing effective prompts.
I would agree if we are at an exploratory phase, but after the exploratory phase, we should standardise the outputs just like we standardise our forms, templates, and procedures.
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dawn dampier Consultant| Consultant North Carolina, United States
Be as specific as you can be - parameters are your friend. Try different parameters if response/solution isn't on target. This is my approach regardless of the task. AI LLM are infused with a LOT of garbage/unproven data - assume this each and every time. Parameters + your validated data keep AI in check as much as possible.
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