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?
Jose Antonio MoralesConsultor, Analista, Evaluador Tecnico| Consultoria y Supervision de Proyectos SACLima, Lim, Peru
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.
speaking to GenAI engine as I would speak to another human, is not the same. Saving Changes...
Adding clear context, constraints, and examples can drastically improve the output. Instead of a vague prompt, specify the audience, goal, tone. Even a few extra details can make the response much more relevant and accurate.
In my experience, the quality of the output improves significantly when the prompt is structured with greater precision. What works best for me is defining the AI’s role, the context, the objective, the required activities, the expected result, and the desired output format. I have also found that an iterative approach produces better results: working step by step, reviewing each response, and providing clear feedback when the AI is either aligned with or moving away from the expected outcome. In summary, a good prompt is not just a well-written question; it is a clear specification of the work, combined with continuous refinement through feedback. Saving Changes...
When creating project updates, I used to ask AI to "summarize project status." After refining the prompt to include the target audience, key focus areas (risks, milestones, dependencies), and preferred format, the output became significantly more actionable and executive-ready. Prompt refinement has reduced editing time and improved the usefulness of AI-generated content Saving Changes...
Dennis BurnsSpecial Projects Manager| ABM Building & IndustryLong Beach, Ca, United States
Jun 21, 2024 7:28 AM
Replying to Sergio Luis Conte
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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).
Refining prompts helps me extensively. There are times when I find that the AI is feeding back incomplete information, so I thoughtfully provide more information.
Saving Changes...
Maged FowzyProject Manager| United Arab AluminiumAjman, Ajman, United Arab Emirates
The more you refine, the more you get accurate answers.
In my experience with GenAI, I have seen a significant difference when I refine a prompt by adding the right context. I may start with a simple question, but the output improves considerably when I clearly explain the objective, background, expected role of the AI, constraints, and desired outcome. I also use follow-up prompts to challenge assumptions, ask for deeper analysis, or simplify the response for a specific audience. For example, instead of simply asking AI to identify project risks, providing the project context, dependencies, timelines, known constraints, and asking it to avoid assumptions produces a much more relevant risk assessment. One thing I have learned is that prompting is not always a one-time activity. I see it more as an iterative conversation provide context, review the response, refine the prompt, and validate the final output.
The quality of the context we provide often determines the quality of the outcome we receive. Saving Changes...
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