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?
We may rarely get the needed output in the first go for a complex analysis/task. Refining the prompt helps in getting the desired output and with each iteration/refinement we can evaluate the accuracy/desirability of the output generated and then tune the prompt further. This is an art 😊
In my experience with GenAI, I have noticed that refining a prompt drastically changed the output quality of the output that it gave me. If you write incomplete phrases GenAI does not return full responses. Additionally, sometimes, you cannot duplicate the same initial output if you do not use the same prompt. Saving Changes...
Increasing specifity and more context leads to more accurate and refined results/output. I am not familiar with the diverse frameworks provided by Sergio Luis Conte; speaking to GenAI engine as I would speak to another human (thus, providing context and sufficient level of detail) provides great outputs.
It makes our work very efficient when prompted clearly. Not being clear enough might cause any delay in getting some informations
“Create a Level 2 WBS for a website development project, broken into: planning, UI/UX design, backend development, frontend development, testing, deployment, and project closeout. Present it in an indented list.”
Refining prompts is like defining clear project requirements. Vague inputs yield generic fluff, but specifying constraints, format, and audience transforms basic outlines into comprehensive, actionable project plans. This precision eliminates rework, ensuring the final output aligns perfectly with stakeholder expectations and saves valuable team time.
Being new to GenAI, and being initially hesitant to use it, I am much more excited about the possibilities after using the prompt engineering tools I have learned in the "Prompt Engineering for Project Managers" course. Prior to taking this course, I relied on my intuition to engage with GenAI. This gave me responses that were thought-provoking but not very practical. By simply using the RTF method (for the first time), PMI Infinity produced a table showing roles and responsibilities for my team that I can actually use. Further refining my request with project data will further help refine the product. 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).
I had just been putting vague prompts in, but I think the structure of RTF and CREATE have really helped to get meaningful output quicker Saving Changes...
Anonymous
span style="background-color: rgb(255, 255, 255); color: rgb(10, 19, 23);"Refining a prompt in GenAI can drastically improve output quality by making the AI’s responses more accurate, relevant, and actionable. In my experience, even small adjustments—such as clarifying the context, specifying the desired format, or adding concrete examples—can transform vague or generic answers into precise, insightful, and tailored results. This iterative process of prompt refinement helps the AI better understand your intent, reduces ambiguity, and ensures the output aligns closely with your original goals, ultimately saving time and increasing the value of the AI’s assistance./span
Saving Changes...
Heather BrinkOwner/CEO| Brink PM Solutions LLCBlacklick, Oh, United States
Jun 21, 2024 9:36 AM
Replying to Eduard Hernandez
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Increasing specifity and more context leads to more accurate and refined results/output. I am not familiar with the diverse frameworks provided by Sergio Luis Conte; speaking to GenAI engine as I would speak to another human (thus, providing context and sufficient level of detail) provides great outputs.
Agree, specific prompts make for better responses.
Generative AI predicts and produces content based on patterns, while traditional AI focuses on classification, prediction, or decision-making within predefined rules. Generative AI creates new outputs; traditional AI analyzes existing data to provide answers or insights. Saving Changes...
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