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?
Siva SubbiahPMO Lead| Senvion Wind technologies Pvt LimitedMumbai, India
A vague or overly simple prompt, such as "Write about climate change," will produce a generic, high-level response. The AI model, lacking specific instructions, will draw from its vast training data to provide a broad summary that may not be relevant to the user's needs. This can be likened to asking a person, "Tell me about cars." They might talk about the history of the automobile, different types of engines, or the most popular models, but without more context, the answer is unlikely to be what you were looking for. Saving Changes...
Siva SubbiahPMO Lead| Senvion Wind technologies Pvt LimitedMumbai, India
A vague or overly simple prompt, such as "Write about climate change," will produce a generic, high-level response. The AI model, lacking specific instructions, will draw from its vast training data to provide a broad summary that may not be relevant to the user's needs. This can be likened to asking a person, "Tell me about cars." They might talk about the history of the automobile, different types of engines, or the most popular models, but without more context, the answer is unlikely to be what you were looking for.
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1 reply by Ronald Cairo
Aug 25, 2025 6:13 AM
Ronald Cairo
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The more "simple and open" the question, the more broad and generic the answers will be; consequently, the more "specific and with subtasks" the answers will be, the more concise they will be; so, while the prompt is simple, clear, direct and specific (delving into some points), the resulting answers will be more compact, defined and applicable to obtain a desired value.
Saving Changes...
Ronald CairoSpecialist Engineer| Ministry of Housing, Construction and Sanitation of PeruElkridge, MD, United States
Aug 25, 2025 3:15 AM
Replying to Siva Subbiah
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A vague or overly simple prompt, such as "Write about climate change," will produce a generic, high-level response. The AI model, lacking specific instructions, will draw from its vast training data to provide a broad summary that may not be relevant to the user's needs. This can be likened to asking a person, "Tell me about cars." They might talk about the history of the automobile, different types of engines, or the most popular models, but without more context, the answer is unlikely to be what you were looking for.
The more "simple and open" the question, the more broad and generic the answers will be; consequently, the more "specific and with subtasks" the answers will be, the more concise they will be; so, while the prompt is simple, clear, direct and specific (delving into some points), the resulting answers will be more compact, defined and applicable to obtain a desired value. Saving Changes...
Khalid IbrahimEndpoint System Support Analyst| Tangerine BankToronto, Canada
In my experience, refining prompts has made a huge difference in output quality. For example, when I asked GenAI to “summarize project risks,” the result was too generic. After rephrasing to “You are a project manager. Identify the top 5 risk factors for project delays in IT system upgrades and present them in a table with likelihood and impact”, the output became structured, relevant, and actionable.
Adding role, context, and format transformed the response from vague text into a practical project management tool.
Saving Changes...
Eugene De WeeHead of PMO | Programme & Project Delivery| Independent Consultant – PMO, Programme & Project DeliveryJohannesburg, Gauteng, South Africa
In my experience, refining a prompt can be the difference between a generic answer and a game-changer.
As an example: I initially asked an AI tool to “summarize project risks.” The output was neat but surface-level.
When I reframed the prompt to:
>>> “Summarize project risks by category (financial, technical, stakeholder) with likelihood, impact, and recommended mitigations, tailored to an executive steering committee,”…the quality shifted completely. The output was structured, prioritized, and ready for decision-making.
The key change was moving from vague intent to outcome-oriented instruction. By embedding context (audience, format, decision purpose), I got outputs I could actually plug into governance meetings without rework.
For me, the lesson is clear: the quality of the question determines the strategic value of the answer.
Saving Changes...
Farhana AhmedProject Manager| BardavonKs, United States
With GenAI, refining a prompt drastically changed the output by catering the output to my needs. Providing examples also helped the quality of the output drastically. Saving Changes...
It is important to remember this: generative AI is just "predictive text with storoids". Obviously not only text will be the result. BUT the important thing is the answer will just to complete your question (prompt) with the things that have more probability to complete it. You can manage it using some of the parameters like temperature. So, it is very important when creating the prompt to put clear the role, the place where the role works/live/etc, the task the role has to accomplish and the format of the answer. This is an example of R-T-F. You have to eliminate as ambiguity as possible. If not, then hallucinations will happened.
Great insight. Thanks for sharing. I am on a journey to learn more. Saving Changes...
Refining a prompt turns generic outputs into precise, actionable insights. By adding context, clear criteria, and specific objectives, AI shifts from vague responses to relevant analyses, useful reports, and recommendations aligned with project management needs. In essence, a well-crafted prompt transforms AI from a text generator into a strategic tool. Saving Changes...
Refining the message not only improves the quality of the result, but also saves time, reduces corrections, and ensures alignment with the actual purpose.
The quality of the input determines the quality of the output. Saving Changes...
In my experience, refining a prompt with GenAI has drastically improved the accuracy and usability of outputs, especially in complex safety and engineering projects.
At first, I used to overload prompts by asking the AI to analyze risks, summarize findings, build a mitigation plan, and format everything at once. The results were incomplete and lacked depth. By applying prompt chaining and formulas like CREATE (Character, Request, Examples, Adjustments, Tone, End Goal), I started breaking large tasks into smaller subtasks.
For example, in a project about implementing intelligent camera systems to detect driver fatigue in oil & gas operations, I first asked the AI to identify risks, then in the next prompt to map technologies available, and only after that to propose an implementation roadmap. Each response built upon the previous one, leading to a structured and actionable plan.
This approach not only improved clarity and relevance but also allowed me to provide targeted feedback and continuously refine the outputs, saving time and increasing the value of AI in project management.
Key learning: Being specific, concise, and iterative with prompts transforms AI from giving generic outputs into producing tailored insights aligned with real project needs.
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