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?
The quality of your prompt and how you sequence them is key to the quality of the output. In my experience, lumping the prompts together doesn't yield quality results. Prompt Chaining is the way to go. Saving Changes...
This varies on the task objective and complexity level.
Generally, it improves the quality, but it is not a cross-the-board. Saving Changes...
Madhusudan TirunahariGroup Manager - IT| AtoS Global IT Solutions and Services Pvt LtdPune, Maharashtra, India
Refining a prompt significantly enhances output quality by providing clearer context, reducing ambiguity, and aligning the AI’s response with specific goals. This leads to more accurate, relevant, and useful results, ultimately improving efficiency and decision-making in project management.
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).
Does a prompt framework has specific application to a specific industry? or Is only applicable to a project? 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.
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).
In my experience, refining prompts brings about impeccable and quality responses and outputs. Saving Changes...
Writing (or engineering :)) a prompt with specific constraints and context is crucial to receive a relevant response. In my experience, reviewing a broad range of low to high-quality prompts, it is important to especially specify abbreviations since they could mean different things in different context to minimize AI hallucinations or an irrelevant response. 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.
Agreed and two things assist me with producing more accurate responses by providing more specificity and context to my prompts. Uploading files as examples or more data and speaking to colleagues or others to also review so I can get another human opinion on the request. Saving Changes...
Angel YamadaConsultant| Angel YamadaBuenos Aires, C.A.B.A., Argentina
I am just learning, but in my short experience, I have seen that giving the right context and examples tends to bring the results to where I wanted to. Also refining and discarding the non acceptable results trough prompt chaining. Saving Changes...
"Far out in the uncharted backwaters of the unfashionable end of the Western Spiral arm of the galaxy lies a small unregarded yellow sun. Orbiting this at a distance of roughly 98 million miles is an utterly insignificant little blue-green planet whose ape-descended life forms are so amazingly primitive that they still think digital watches are a pretty neat idea..."
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