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?
Using AI can significantly enhance decision making, forecasting, and risk management but only when used responsibly. I proactively question the AI's assumptions and check for bias that could skew results, especially in risk assessments or stakeholder analyses.
AI not a replacement for human judgment, So I always review outputs with SMEs or team members, especially when decisions carry significant impact.
I would ask for reliable sources to provide. Then I would check the sources and see if the information are accurate or not. Saving Changes...
Leonard MarcheseFounder & President| Rethink Inc.Chicago, Il, United States
Use a specific role. Provide examples. Apply evaluation criteria. Flip the prompt and have AI ask the question or provide feedback. Request a strategy to see if the response plays out. I like to apply reverse think, view the opposite and see how the responses align and differ. If way off, then there could be a flaw in the original response. Saving Changes...
It depends on how you interact with AI by providing specific prompt with create pattern. Saving Changes...
Anonymous
Grear course! Saving Changes...
Ishwar SinghProject Management| Kyndryl Solution Pvt LtdGhazibad, Uttar Pradesh, India
Providing the specific context in clear and concise way is essential. Saving Changes...
Asif KhanProduct, Program, Customer and Executive Mgmt| IBMAshburn, Va, United States
Jun 11, 2024 2:25 PM
Replying to Melissa Stockbridge
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Some of my items may be redundant but the most important things in my experience so far is:
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
Experiment and refine as you go
I've found breaking down big problems can be better refined by chunking the whole into natural sections and working to refine each section and then working to put them back together.
In addition the course does touch the topic of AI Hallucinations, Non Current Data, misalignment, etc. and associated problems. The best practices or solutions provides additional measures to address problems and symptoms of vague ,outdated, conflicting incomplete responses.
Patterns such a Tree of thoughts, Personas, ReAct etc. provides additional methods of refinements leading to better refined and complete responses. Saving Changes...
Hi PMI community, best practices for ensuring results we would receive to be accurate, relevant, and aligned from AI systems would be to necessitate that we are well versed in the subject as a SME. As AI systems are still developing and learning via the input of humans. Thus we can't solely entrust AI's results completely until more data is given, but in order to validate those results it bottles back to us needing to be well versed in the subject to avoid AI's assumptions at times. Saving Changes...
- Be Clear and Specific in Your Prompts
- Verify Information from Multiple Sources
- Align with Your Goals
- Use Critical Thinking
- Iterate and Refine
- Use AI as a Collaborator, Not a Replacement Saving Changes...
AI is undeniably a powerful tool that enables accelerating productivity, enhancing creativity, and unlocking insights across countless domains. Its usefulness is evident, from streamlining workflows to aiding in complex decision-making. However, it is important to balance that enthusiasm with critical awareness. Overreliance on AI, especially without understanding its limitations, risks amplifying biases, reinforcing errors, or dulling essential human judgment. As we integrate AI deeper into our lives, the goal should not be blind trust, but thoughtful collaboration, leveraging its strengths while staying actively engaged and responsible in how we use it. Saving Changes...
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