Director, Learning Design & Development| PMIAsheville, 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?
In my experience, refining prompts has been the difference between outputs that are generic and unfocused and those that are precise, relevant, and highly useful. By providing clear, specific, and directive in my prompts, I have been able to guide the AI to produce results that better meet my needs and expectations. Saving Changes...
Babatunde BalogunSenior Administrator| Norfolk and Suffolk NHS Foundation Trust
It is very important that Generative AI can’t read your mine set from the front of your computer to know the exact question you are asking. It can only know what answer(s) you wanted through the way and manner your question was constructed. Generative AI needed to be tailored to through input and adding all necessary information to give our desired result. Saving Changes...
I have achieved more accurate, reliable, and relevant outputs from GenAI by explicitly asking for academic references in my prompts. This approach enhances the quality of the information and ensures that the data used is more credible and well-sourced. However, I encourage you to verify the sources yourself. Saving Changes...
A prompt that doesn’t specify tone or style might produce an output that’s too formal, casual, or otherwise mismatched with the intended audience.
Specifying the desired tone (e.g., professional, conversational, humorous) ensures that the output resonates better with the target audience. Saving Changes...
Tareq A. Al BehairiProject Management Consultant| Independent Consultants and TrainerGcc, Kuwait
I believe that improving and increasing the output quality from Gen AI involves setting a comprehensive plan for prompting into LLM. This plan should be thorough enough to yield improved responses and include selecting a fit-for-purpose formula from best practices or even creating an innovative formula from scratch. For example, if you are working on a project management task, you might start by using established frameworks like PMBOK guidelines and then adapt them to your specific project needs.
Additionally, chaining the prompts effectively is crucial. This means taking incremental steps, much like teaching a baby to walk. For instance, if you are developing a project timeline, you might start with a broad prompt like "What are the key phases in project management?" and then narrow it down with more specific prompts such as "What are the best practices for scheduling in the planning phase?" and "How can I mitigate risks during the execution phase?" This step-by-step approach ensures that each response builds on the previous one, leading to more refined and accurate outputs.
Maintaining an iterative style until satisfactory results are achieved is also essential. This involves continuously refining your prompts based on the feedback and results you receive. For example, if the initial AI-generated project timeline is too generic, you can iteratively refine your prompts to include more specific details about your project's scope, resources, and constraints. Saving Changes...
I have had little experience using GenAI such as ChatGPT, but the few times I have done so, I did so without knowing how to properly structure the prompts to obtain better responses.
I think that, intuitively, I sometimes made improvement questions based on some things I read or that I managed to find on the Internet, but without having a structure or a defined way to do it, such as the RTF or the CREATE technique that we saw in this course. I did notice, when making changes or refining the prompt, that I obtained better results in the adjusted response.
I cannot imagine the change I would achieve if I mastered Prompt Engineering.
...
1 reply by Camimlo Sarmiento
Apr 24, 2025 8:26 PM
Camimlo Sarmiento
...
In my experience leading HubSpot implementations globally, refining prompts in GenAI is like fine-tuning a project brief, it changes everything. An initial, vague prompt might yield generic output, but layering in context, expected tone, and formatting transforms the results into something actionable and client-ready. I’ve seen outputs shift from surface-level summaries to strategic, insight-driven content just by restructuring the prompt to mirror stakeholder expectations. Prompt iteration feels a lot like agile sprints: define, test, refine. Sometimes, what unlocks quality is as simple as stating the role the AI should adopt or providing a clear use case. It’s not just about getting any answer, it’s about getting the right one. Prompt engineering has become an essential skill for maximizing the value GenAI brings to project management and client communication.
Saving Changes...
Cynthia HoyosSenior Program Manager Leader| JazwaresPembroke Pines, Fl, United States
Like Sergio mentioned above regarding the importance of the role and the format, I used it when I asked it to summarize the transcript using the user story format for conditions of satisfaction. I actually had to train myself to run the sessions with the same script to improve the output with my prompt. Saving Changes...
Being concise and specific, plus dividing complex prompts in tasks allowed me to obtain better answers tailored to the context and with an output of higher quality. Also documenting prompts techniques used in a dashboard or sheet for future reference helped me to improve the quality of answers Saving Changes...
francis follyCategory Lead| Government of ManitobaWinkler, Manitoba, Canada
Effective prompts remind me of good user story techniques. State the persona, what you want to accomplish and why. Then built out the details with a set of well defined acceptance criteria. From there, AI builds something and you can iteratively tweak it. Saving Changes...
PMI Team Member
