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 normal life also you get different answer for the same question, if it is presented in different style, language, to different people and in different context (setup, business, your situation etc). Therefore, learn to think deeper, communicate better with fellow human being and become more structured in your own thinking. Working with machine can be easier than working with machines. Templates are crutches, they are useful when you are starting walking or too old to walk. But once you know walking, then using crutches is limiting yourself. If you use your full creative potential fully then you will run, you may be an Olympic winner, may be your fly. Around 2 years back, when I was doing my doctorate in AI/NLP that time people were saying there is new course on Prompt Engineering. I was laughing, what! If human could communicate better with human then we don't need this course. Sometimes I feel a great project manager is already a good communicator, 90% communication. Unfortunately it is difficult to find a great project managers. Saving Changes...
Valerie Williams-SanchezPrincipal Consultant| Valorena Online, L.L.C.Palisades, Ny, United States
The difference can be like night and day! And it's a difference I'm noticing as I use and learn more about the capabilities of AI & LLMs. for example, just changing the way in which I relate to AI, as a being not simply as a machine, has improved the quality of my out puts. That is when I take a conversational tone, even in minor tasks and asks, I get more engaged response. I have an upcoming project that I hope to implement more of the PMI tactics in my use and asks of AI, includind a RACI style chart for the project stakeholders! This aspect of the project has now reinvigorated my interest int he project! Saving Changes...
Tera MontgomeryProgram Manager| QuarterhillAustin, United States
Refining a prompt in GenAI can drastically improve output quality by ensuring clarity, specificity, and alignment with the desired outcome, transforming vague or generic responses into actionable, relevant insights. Iteratively adjusting prompts allows project managers to fine-tune the system's focus, extract deeper insights, and address nuanced challenges. This process highlights the value of AI as a dynamic tool for enhancing decision-making and productivity. However, risks such as misinterpretation or unintended biases in outputs can be managed by testing multiple iterations, validating results against known standards, and maintaining human oversight to ensure the AI supports, rather than replaces, critical thinking. Saving Changes...
In my experience with *Generative AI (GenAI)*, refining a prompt has a profound impact on the output quality, often transforming it from generic or vague results to highly relevant and insightful content. By providing *clearer context*, *specific instructions*, and *well-defined objectives* in the prompt, I’ve seen significant improvements in the AI’s ability to deliver precise, actionable, and aligned outputs. This iterative process of fine-tuning allows the model to better understand the nuances of the project requirements, leading to more accurate predictions, refined analyses, and tailored recommendations that directly contribute to *informed decision-making* in complex project management scenarios. The clarity and specificity of the prompt directly shape the AI’s ability to provide value, demonstrating how small adjustments in prompt engineering can drastically enhance the quality of the results. Saving Changes...
Design your prompt in a clear, succinct, approachable, and targeted way for GENAI to return an excellent output response. In a chain prompting, as the Genai delivers the response you require at every chain point, appreciate it like you appreciate humans before the next prompt. Also enjoy it even if you do not get an accurate response because Genai is quite scientific, and obeys the law of conservative energy where it processes the data received, and delivers the best output based on the data. Saving Changes...
I build GENAI based solution and refining prompt always helps in getting desired results. Saving Changes...
Donna OttDirector of PM/PMO| ETSMaple Shade, Nj, United States
Do now feel I can answer this question as I have not done an complex prompts. Mine have mostly been RTFs and it is easy to evaluate and adjust those. I am having difficulty understanding the chaining of task concepts. Can this be used with RFT prompts? Saving Changes...
There are framewoks to create prompt. This is part of the Prompt Desing discipline. Those that gave me and the initiatives where I was included are:R-T-F (Role-Task-Format), T-A-G (Task, action, goal), B-A-B (Before, after, bridge), C-A-R-E (context, action, result, example), R-I-S-E (role, input, steps, expectations).
Thank you for sharing those frameworks! It's exciting to see Prompt Design formalizing through these methodologies. A few key questions and thoughts to consider: 1. Adaptability: How well do these frameworks work across industries or disciplines? Can elements from different frameworks be combined for better outcomes? 2. Efficiency: How do we measure the success of these frameworks? Are some better suited for collaborative versus individual use? 3. Evolution: Could hybrid or iterative frameworks emerge that balance structure and flexibility? Have you seen any new approaches recently? 4. Stakeholder Engagement: Do these frameworks account for diverse user input and cultural or ethical nuances? 5. Practicality: Could simpler heuristics (e.g., "why-what-how" cycles) work as efficient alternatives? 6. Integration: How well do these frameworks align with broader processes, like AI lifecycle management or project tools? Lastly, while these frameworks provide structure, is there a risk of over-complicating what might thrive as a creative and intuitive process? How do you balance structured approaches with flexibility in practice?
P.D.: The answer was got it with ChatGPT in two laps. :) 1. Hi, please, you as an AI PMP expert rol, I need some ideas to answer the point view of someone."<< The text >>". The answer would be oriented in a questioning view with some alternative views. 2. Just get a brief of first
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