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?
Mark van RijnbachSenior Project Coordinator| Cognizant Technology SolutionsAmsterdam, Netherlands
Jun 11, 2024 11:22 AM
Replying to Omar Jabbar
...
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,
Agree to keep it simple if you are new using Gen AI. Think of what you want to achieve and how the result should look like. Start small, look at the outcome and add some more context. Check and review it again etc, etc.
Use it like a continuous improvement process and check the outcome. Get the right (technical) people to verify your outcome.
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Andrea JacksonProgram Manager| Hewlett Packard EnterpriseDawsonville, GA, United States
Be specific, validate the answers and tailor with your iterations until the result is desired
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Samiran DasManaged Service Leader| Ernst & YoungKolkata, West Bengal, India
To my mind, using RTF or CREATE formula for PROMPT creation are the best practices
To ensure AI outputs are accurate, relevant, and aligned with your goals, begin by clearly defining your objective, constraints, audience, and desired format so the system understands the intended outcome. Avoid vague prompts; instead, provide specific context and criteria for evaluation. Ask the system to state its assumptions, identify potential uncertainties, and distinguish facts from interpretation to reduce hidden inferences and overconfidence. When accuracy matters, request sources or independently verify key claims, especially for time-sensitive, technical, legal, or financial topics. Iteratively refine responses by clarifying ambiguities, correcting misunderstandings, and narrowing scope, treating AI as a collaborative draft generator rather than a final authority. Finally, apply critical thinking; cross-check important conclusions, watch for overly confident language without evidence, and ensure the output aligns with your original intent before relying on it for decisions.
Be precise and specific in your instructions. Run test case against known responses. Consult subject matter and project experts. Update with new information and data. Provide constraints round data and information to be considered.
Providing the specific context in clear and consise way is essential.
Be precise and specific in your instructions. Run test case against known responses. Consult subject matter and project experts. Update with new information and data. Provide constraints round data and information to be considered. Saving Changes...
When using generative AI, utilizing some of the prompting techniques such as CREATE to ensure that prompts are accurate and tasks are well described helps to ensure that the accurate responses are received. Not only that, applying feedback through interactive prompting allows us to force the model's responses to align with our original goals or objectives for the task. Saving Changes...