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?
AI is broader aspect but for Gen AI, always think that you are talking to a layman to whom we need to provide / equip with more details in order to get expected results. So have patience when we spend time in choosing Prompts, read output and revise the Prompts in order to achieve better results.
Be specific to choose appropriate prompts (RTF, CREATE, SMART, STAR, ABCD, PEAR, SCARF, etc.) and always remember, "When data is translated into information and combined with experience and knowledge, it leads to better outcomes in the pursuit of wisdom" (statement credit goes to my manager who insists on this statement). Saving Changes...
In project management, validating and verifying Generative AI outputs is critical to safeguard deliverable quality and stakeholder trust. This requires setting clear acceptance criteria, applying rigorous testing protocols within the project lifecycle, and continuously refining prompts and processes to minimize risks and ensure alignment with project objectives. Saving Changes...
Give precise instructions to the AI application, assigning a specific role to the AI with specific responsibilities and constraints. Give constant input and feedback to the AI application so it learns how to better respond to your project needs. Saving Changes...
When using AI systems, it’s important to be clear and specific about what you need, so the answers you get are closer to your goals. Always take a moment to check the information against trusted sources, especially if you’re using it to make decisions. Sometimes the first response might not be perfect, so don’t hesitate to refine your question or ask in a different way. Keep your main objective in mind and compare the AI’s response to what you originally wanted, to make sure it’s useful and relevant. And finally, remember that AI isn’t flawless—it can make mistakes or reflect biases—so combining its input with your own judgment will always give better, more reliable results. Saving Changes...
Jason MrozLearning and Development Manager| Compass IncBeltsville, Md, United States
Jun 08, 2024 1:37 PM
Replying to Keith Novak
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Like with any new tool, you need to test the results before you scale up.
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.
Yes! I think so many people want AI to be perfect and immediate and while it is a tool that greatly improves speed of work and efficiency, the subtle refinements made by putting in the time up front to check progress throughout the process of iterative generation keeps your outcomes on track. A small deviation from your course can have a big impact when considered at scale. Think about when you're driving. You're constantly adjusting the wheel a little here and there to keep the car between the lines. If you don't, you're going to end up drifting into another lane and causing an accident. Saving Changes...
When using AI, the two best techniques are being clear about your context and goals and asking for structured answers. If you tell the AI exactly what you’re trying to achieve and give it the right background, it’s much more likely to stay on track and give you relevant results. And if you ask for the response in a clear format—like bullet points, tables, or step-by-step lists—it makes the answer easier to check, compare, and use. These two simple habits go a long way in making sure the results are both accurate and useful. Another best practice would be to ask AI to evaluate it's own response with different perspectives Saving Changes...
AI Tips: Clear goals, precise prompts, verify, iterate. Saving Changes...
Anonymous
1. Be clear: Define your objective and expected output.
2. Add context: Include project details, constraints, and priorities.
3. Iterate: Use drafts and refinements instead of expecting perfection in one try.
4. Assign roles: Guide responses by asking the AI to act as a specific expert.
5. Validate: Cross-check outputs with reliable data or experts.
6. Ask for transparency: Have the AI explain assumptions and reasoning.
7. Update frequently: Restate new goals as project objectives change.
8. Reuse what works: Save effective prompt structures as templates. Saving Changes...
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