Director, Learning Design & Development| PMIAsheville, NC, United States
Validating and checking outputs is critical when working with AI systems like Generative AI. Such validation approaches may include establishing clear criteria, implementing strong testing protocols, and continuous refinement.
In your experience with AI, what are some best practices for ensuring the results you receive are accurate, relevant, and aligned with your original goals?
When writing questions to get answer from AI describe in CREATE model. It's my experience, the best results are achieved the more context I provide to the LLM. Saving Changes...
To ensure AI results are accurate and aligned with your goals, clearly define your objective and provide relevant context in your prompts. Use iterative prompting — review, refine, and re-prompt as needed — to guide the AI toward better outcomes. Finally, always validate the AI’s output against trusted sources or business requirements before acting on it. Saving Changes...
To get accurate and relevant AI results, be clear with your goals, prompt iteratively, fact-check outputs, and always apply your own judgment before using the results Saving Changes...
Hi folks, I think to get accurate and relevant AI results, be clear about your goal and provide specific, context-rich prompts. Always review outputs critically, iterate when needed, and verify important facts—think of AI as a smart assistant, not a final authority. Saving Changes...
Start with Clear Objectives, Use Iterative Prompting, Leverage Flipped Interaction, Leverage Flipped Interaction, Validate with Human Oversight, Incorporate Version Control and Logging, Set Boundaries for the AI, and Test Across Multiple Scenarios.
In short, combining clear communication, adaptive prompting, human validation, and iterative refinement is key to aligning AI results with project goals in a fast-moving environment. Saving Changes...
Start with Clear Objectives, Use Iterative Prompting, Leverage Flipped Interaction, Leverage Flipped Interaction, Validate with Human Oversight, Incorporate Version Control and Logging, Set Boundaries for the AI, and Test Across Multiple Scenarios.
In short, combining clear communication, adaptive prompting, human validation, and iterative refinement is key to aligning AI results with project goals in a fast-moving environment. Saving Changes...
A very good question and also difficult to answer as well. However you have to go to the basics and say as far as you are concerned, how well are you versed with the subject at hand ?. There are facts which the AI will generate and if you can verify these facts the more reliable the generated response will be. The fewer the facts then it means that the Generative AI response is far from meeting your original goals. Then it becomes very critical that you review the accuracy , relevancy and the alignment of the response to your original need. Unfortunately there are no clearly defined metrics that one can use a model to evaluate an AI generated response. So from my personal experience I basically restrict AI to an area where i have sound knowledge of , else it becomes almost impossible to verify details generated by an AI if you venture into unchartered territory. However with long usage and exposure your confidence also tend to increase as well. The best practice and protocol to follow would be to consult subject matter expects to validate the AI generated response before making critical decisions based on it to avoid any inherent associated risks which you might be not aware of.
The question is pretty difficult to address, given some limitations that has been associated with the use of AI. Nonetheless, an iterative and comparison of the outputs could increase the chances of getting a reliable results. Saving Changes...
I don't disagree with the answers above, but I keep it very simple. Make sure your data is clean, ask specific questions, and review the outcome. All of this will depend on the AI tools you are using and your needs for using them. Once you have this figured out, you will be good to go.
Continuing review and improvement are essential in this case.
I hope that helps.
Regards,
100% right on that. Data has to be clean, the info we are feeding or asking should be clear, concise and specific to what goal we are trying to achieve Saving Changes...
Anonymous
Start the prompt by clearly defining the task you want the AI to accomplish. Describe exactly what you need, whether it’s drafting a writeup or report, data trend analysis, or creating an action plan. Next, provide all relevant context: including background, key data, and any constraints that could affect the output. This ensures the AI has a comprehensive picture to tailor its response to the specific needs.
When the task involves multiple steps or complex requirements, break it into a sequence of smaller checkpoints. An example is to ask the AI first to categorize a set of data first, then to sort it, and finally to clean and normalize the dataset. This makes it easier to validate each part of the response and the correct course of action. Specify your output requirements: the format you prefer (paragraph, table, html), the tone or style (concise, casual, conversational), and any templates or examples you’d like the AI to follow.
For a water-treatment upgrade project, you might enter: “Draft a three-pages executive summary on our upcoming filter upgrade at the WTP X. Include an overview of scope, three key risks, analysis, and recommended next steps. Write in a clear, professional tone using the attached ‘Executive Summary’ template".
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