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 working with AI, I always tell the who I am or what role AI partner is representing, who my audience is, the tone I am looking for if I am looking to rewrite an email and how detailed I would like to be. I will upload pertinent documents or information or specify documents to reference. I will continue to refine results as the results are returned. Saving Changes...
Think about if you were to manually model a very complex problem in a spreadsheet. You don't build all the links and formulas first and then evaluate your final output. You build and test sections of the bigger solution first and then add on layers once you have validated the functionality.
When I use AI, I get the best results by providing precise data, along with instructions on what I want to do with it and the outcome I want to achieve. Based on that information, I then provide further instructions on how to proceed.
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Anonymous
To ensure AI-generated results are accurate, relevant, and aligned with your goals, follow these best practices:
Write Clear, Contextual Prompts: Clearly define the objective, role, and format you expect from the AI to minimize ambiguity.
Maintain a Human-in-the-Loop: Always critically review, cross-reference, and audit AI outputs rather than accepting them blindly.
Establish Guardrails and Baselines: Define explicit criteria or target metrics beforehand so you can accurately measure if the AI's response aligns with your original goal.
AI responses are nothing more than a reaction to the prompts provided by the user. Therefore, the accuracy, reliability, relevance, and alignment of the response depend on how effectively the prompt is written. A strong prompt should clearly define the persona, objectives, assumptions, constraints, validation criteria, expected output format, and intended stakeholders. Simply put, better prompts lead to better outcomes—just as clear requirements lead to successful project delivery. Saving Changes...
Karin PitmanProject Manager| Central New Mexico Community CollegeAlbuquerque, NM, United States
Hard to add much to the tons of responses in this thread, but as I understand it, asking for answers that are concise and to the point, using examples of similar data, giving good context for a question, setting the tone I would like used, working with the LLM to refine and test any answers provided, etc. are all some of the best practices I'm learning.
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Anonymous
Be precise and clear.
Be sure you explain jargon or specialized terminology
Provide the context for all of your requests
Be sure you provide the outcomes you are expecting
AI is a broader term. Generative AI is just an ancient model but everything "explode" when Google published the new architecture called transformer in 2017. So, with that said, take into account that generative AI is just "predictive test with steroids" just simplifying the model. With that said, two key points has to be taking into account when somebody works with AI: 1-human in the loop. 2-AI without Data (today called data science discipline or big data or whatever) is the same thing that live without oxygen. Talking about generative AI all related to technology has almost not impact with relation to all related to non-technological roles and activities. What you stated about accuracy and things like that are easy to implement because there are a lot inside disciplines like statistics. Most of them to make things "a priori" to prevent instead of cure. Few organizations taking into account that when generative AI environments are put in place almost a new business unit has to be created where roles like lawyers, linguistic, diversity and inclusion specialist must be hire to help on put it in place.
When using AI systems, the most important practice is to craft clear, specific prompts that include relevant context. The more precise your input, the more accurate and useful the output. You should always verify AI-generated results against your own knowledge, data, or trusted sources, since AI can produce plausible-sounding but incorrect information. Finally, treat AI as an iterative collaborator; refine your prompts based on initial outputs, and never delegate final judgment to the system, as human oversight and accountability remain essential. Saving Changes...
"We should be careful to get out of an experience only the wisdom that is in it - and stop there; lest we be like the cat that sits down on a hot stove-lid. She will never sit down on a hot stove-lid again, and that is well; but also she will never sit down on a cold one anymore."
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