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?
my experience is:
1. Break the problem into small parts.
2. Provide the context for the inquiry.
3. Adjust as you go.
4. Verify the authenticity of what the AI ​​says.
5. Be polite. Saving Changes...
When working with AI systems it is critical to ensure that the information provided /requested is clear, specific yet detailed enough to generate the appropriate response, This prevents the “Garbage in garbage out” output.
Additionally there should be continuous reviview/validation and adjustment to the data given to and generated from these AI systems.
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Robert AbeytaSenior Director of Supply Chain| Botanix PharmaceuticalsGilbert, AZ, United States
Checking in with stakeholders will help to ensure that the goals of the AI system were used correctly, and may also help with the output in a format that is understandable to the rest of the RACI identified participants. Saving Changes...
Anonymous
Give feedback so the developers can make adjustments Saving Changes...
As an experienced project manager utilizing AI technologies, I have found that following the CREATE Prompt analysis framework is essential for ensuring the results I receive are accurate, relevant, and aligned with my original goals. Here’s a narrative that outlines this approach in a human-readable format, including examples of benefits achieved by implementing Oracle EPM with AI assistance:
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When I first started integrating AI into my project management processes, I quickly realized the importance of setting clear and specific goals. This step is crucial because it helps in aligning the AI system's outputs with our organizational objectives. For instance, when we implemented Oracle EPM (Enterprise Performance Management) with AI assistance for project scheduling, we defined SMART goals to ensure the tool met our needs effectively.
Critical thinking plays a significant role in interacting with AI systems. I always approach AI-generated information with a healthy dose of skepticism. By scrutinizing the data, questioning assumptions, and ensuring that conclusions are supported by solid evidence, I can validate the accuracy and relevance of the outputs. This practice has saved us from potential pitfalls on numerous occasions.
Iterative refinement is another key aspect of the CREATE framework. I often find myself trying different approaches and asking the right questions to get clearer, more accurate results from the AI system. This iterative process helps in fine-tuning the prompts and improving the overall quality of the outputs. For example, when using Oracle EPM, we iteratively refined our financial forecasting models, which led to more precise budget allocations and resource planning.
Evaluating existing systems is a step I never skip. Conducting a thorough analysis of our current state allows me to determine the necessary steps to achieve our defined goals. This evaluation helps in identifying gaps and areas for improvement, ensuring that the AI implementation is both effective and efficient. With Oracle EPM, this meant assessing our current financial processes and identifying how AI could streamline and enhance them.
Performing a cost-benefit analysis is essential when considering the adoption of AI tools. By using data insights, I can evaluate the potential benefits and drawbacks of implementing these technologies. This analysis helps in making informed decisions that align with our budget and strategic objectives. For instance, implementing Oracle EPM with AI assistance resulted in significant time savings in financial consolidation and reporting, which translated into cost savings and improved decision-making.
Establishing a cross-functional team has been invaluable in our AI implementation process. By bringing together diverse perspectives and expertise, we can address various challenges and ensure a holistic approach to AI integration. This collaborative effort has led to more innovative and effective solutions. In the case of Oracle EPM, our team included finance, IT, and project management professionals, which ensured that all aspects of the implementation were covered.
Ethical considerations are always at the forefront of our AI initiatives. We address potential biases and negative impacts to ensure responsible and ethical use of AI technologies. This approach not only maximizes the benefits but also mitigates potential drawbacks, fostering trust and transparency within our team and stakeholders.
In summary, by following the CREATE Prompt analysis framework—Clear goals, Critical thinking, Iterative refinement, System evaluation, Cost-benefit analysis, Cross-functional teamwork, and Ethical considerations—I have been able to effectively leverage AI tools like Oracle EPM to achieve our organizational objectives. This structured approach ensures that the results we receive are accurate, relevant, and aligned with our original goals, ultimately enhancing our project outcomes and overall efficiency. The benefits achieved include improved financial forecasting, streamlined consolidation and reporting processes, and significant time and cost savings. Saving Changes...
Anonymous
To get accurate and relevant results from AI systems, start by clearly defining your goals and asking specific, well-thought-out questions. Provide context where needed and refine your prompts based on initial outputs. Always validate the information against reliable sources and apply your judgment to ensure it aligns with your objectives.
To receive accurate results, here are some key steps to follow:
- Understand the limitations of the AI system being used
- Clearly define the objectives and success criteria
- Provide clear prompts - RTF or CREATE depending on need
- Validate and double-check AI outputs against trusted sources
- Continuously monitor, adjust, and gather feedback to improve accuracy
- Incorporate AI to complement your expertise
- Ensure transparency, ethical considerations, and data security Saving Changes...
To achieve accurate outcomes with AI systems, follow these best practices:
1. Set Clear Goals: Define desired outcomes to guide expectations.
2. Ensure Data Quality: Use and clean high-quality, relevant data.
3. Monitor Performance: Regularly assess the AI system using metrics.
4. Incorporate Human Oversight: Validate results with human review.
5. Create Feedback Mechanisms: Enable user insights for adjustments.
6. Promote Transparency: Clarify the AI's decision-making process.
7. Consider Ethics: Address privacy, fairness, and accountability.
8. Stay Informed: Keep up with AI developments for improvement.
These practices enhance the accuracy and relevance of AI results.
Whatever we are discussing here can/may be applicable to this topic one or the other way...
In general we can try to apply few techniques in the AI prompt to groom it and get what we need..
Clarity on inputs, and Specificity at its best.
Provide enough context.
Iterative Refinement.
Testing and Validating the responses.
Use feedback from the responses to adjust your prompts.
Avoiding Bias and Be mindful of any potential biases.
Always keep your end goal in mind.
Keep your prompts as simple as possible.
Use of Examples.
Regularly review the effectiveness of your prompts and adapt them as these models are constantly evolving.
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Darren BrownManagement| MilitaryAustin, Texas, United States
I concur with the above responses. Be precise and clear.
Be sure you explain jargon or specialized terminology Saving Changes...
"If you have an important point to make, don't try to be subtle or clever. Use a pile driver. Hit the point once. Then come back and hit it again. Then hit it a third time--a tremendous whack."
PMI Team Member
