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?
Start by making sure your input framework matches the type of output you're looking for. Use different prompting patterns depending on the needs of the project, and don't expect the perfect response on the first try. Iteratively refine your prompts while continuously evaluating the AI's output against your desired deliverable. The more you guide the AI throughout the process, the more accurate, relevant, and useful the final result will be! Saving Changes...
In my experience, refining a prompt has significantly improved the quality of the output. The more detailed, structured, and specific my input is, the more closely the response aligns with what I'm trying to accomplish. I've found that tailoring my prompts based on the situation makes a big difference, whether I need a more casual tone, a specific framework, or a particular format for a deliverable.
Refining prompts has also helped me interpret data more efficiently, identify patterns, and save time by producing outputs that require less revision. I'm continuing to learn that having a clear strategy and framework when using GenAI leads to more accurate, useful, and project-specific results. It's become less about getting the "perfect" response on the first try and more about using an iterative process to guide the AI toward the outcome I need. Saving Changes...
To get accurate, relevant, and goal-aligned results from AI systems, it's important to start with clear and specific prompts that provide enough context, define the desired outcome, and include any constraints or success criteria. Breaking complex tasks into smaller steps often improves quality. It's also essential to verify important information using reliable sources, since AI can occasionally generate incorrect or outdated content. Reviewing and refining prompts based on the AI's responses helps improve results over time. Finally, keeping a human in the loop, especially for critical decisions, ensures that outputs are accurate, ethical, and aligned with the original objectives.
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.
I know we will have to continue to train the LLM to ensure good clean data without an AI hallucinations.
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Marion HuslerIT Professional| EisforEverything LLCDelta, CO, United States
For providing context, you can upload artifacts for AI to reference - Such as a billing matrix that you pull in from GCP or AWS, or a bank of issue number - issue summaries, and linked components to map out a draft dependency matrix.
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Mohammed ElrasheedStrategic Advisor | Business Development and Digital Transformation Consultant| Consulting ServicesRiyadh, Saudi Arabia
Setting clear goals by defining the role, context, desired formats, constraints using the famous prompt formulas or methodologies like RTF, CREATE and avoid vague and generic queries following with illustration examples and desired outputs similarities on top of tracing by cross check and feedback; this will provide the most out of AI that match the prompter’s intent and requires minimal iterative and reworking of the prompt structure.
When using AI systems, best practices for ensuring accurate and relevant results include crafting clear, specific prompts with sufficient context, and iterating on them based on the outputs you receive. Always verify AI-generated results against reliable sources, as AI can produce confident but incorrect information. Keeping your original goal in mind helps you critically evaluate whether the response truly addresses your need, and understanding the limitations of the AI system you are using will help you know when to seek human expertise instead.
When using AI systems, best practices for ensuring accurate and relevant results include crafting clear, specific prompts with sufficient context, and iterating on them based on the outputs you receive. Always verify AI-generated results against reliable sources, as AI can produce confident but incorrect information. Keeping your original goal in mind helps you critically evaluate whether the response truly addresses your need, and understanding the limitations of the AI system you are using will help you know when to seek human expertise instead.
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Theola DuBoseFunctional Manager| BT AmericasLilburn, Ga, United States
To ensure AI-generated results are accurate, relevant, and aligned with project goals, project managers should provide clear objectives, detailed context, and specific formatting requirements while defining any constraints or success criteria upfront. Breaking complex requests into smaller steps, verifying critical facts, and reviewing outputs for alignment with stakeholder expectations helps improve quality and reduce risk. AI responses should be refined iteratively through follow-up prompts and treated as drafts that require professional judgment and validation. Additionally, project managers should protect sensitive information and follow organizational governance policies, using AI as a productivity tool that supports—not replaces—sound project management practices and decision-making. Saving Changes...
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