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?
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,
Always start with a clear purpose. Think of this like setting a destination before a journey. If you’re using AI to write reports, analyse data, or generate ideas, be clear on what you want the AI to do.
This helps you judge whether the output is actually useful.
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,
Always start with a clear purpose. Think of this like setting a destination before a journey. If you’re using AI to write reports, analyse data, or generate ideas, be clear on what you want the AI to do.
This helps you judge whether the output is actually useful.
Saving Changes...
Anonymous
People using AI should not forget that an AI doesn’t actually think. An AI is providing the most statistically-relevant response. That’s why the Persona and character creation help to refine the output and are popular. PMs are often focused on the roles of the different functional areas of a project and are refining the output for the specific stakeholder or customer experience. Saving Changes...
Anonymous
While no single best practice applies universally, the following guidelines can help ensure effective and reliable outcomes when working with AI tools:
Provide Context – Share clear and sufficient background information about the scenario or question.
Select Appropriate Methodology – Use suitable approaches (e.g., RTF, character-based methods, or others) depending on the task.
Define Constraints – Specify limitations, boundaries, or conditions that must be considered.
Support with Valid Examples – Use true and relevant examples to illustrate and validate the response.
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Bhuvaneswari NatarajanPDI Data & BI Integration Architect| Shell India Markets Private LimitedChennai, India
Jun 08, 2024 11:44 AM
Replying to Giorgos Sioutzos
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Providing the specific context in clear and consise way is essential.
I totally agree, If you really want the AI to give your meaningful and effective output for what you are looking for, then it's really important that you be specific with your ask, and the format of output also the role that AI is expected to play when giving you output.. Saving Changes...
Bhuvaneswari NatarajanPDI Data & BI Integration Architect| Shell India Markets Private LimitedChennai, India
Jun 12, 2024 1:31 AM
Replying to Jabin Geevarghese George
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When using AI systems is very hard to set the precision or accuracy of the responses. I love bringing in the Agile mindset here pretty much imagine if you are mentoring someone you do a Q&A and based on the reponses of your Mentee you give the feedback so that Mentee can align his/her thoughts in the direction that we hint similarly review the AI responses and using our rationale judgement
1- Give Feedback to the AI system
2- Rework on your promp and be specific on what is expected
3- Keep it short and conscise, guage the responses and slowly we can tune the AI system in a way to get the best output
4- Now the Tech. Solution that comes in for accuracy is havig specific set of APIs that talk to real and accurate data sources or use 2-3 outputs of LLMs and then analyze and bring the best in output.
Of course, we can give feedback and also re tweak our prompts Saving Changes...
To ensure AI-generated results are accurate and aligned with your goals, start by crafting clear, specific prompts. Provide context or examples to guide the AI’s understanding. Always verify outputs using trusted sources, especially for critical decisions. Refine your queries if results seem off—iteration improves accuracy. Be mindful of biases and limitations in AI systems, and avoid relying solely on them for sensitive or high-stakes matters. Use AI as a collaborative tool, not a final authority. Finally, document your goals and criteria beforehand to stay focused and evaluate whether the AI’s responses truly meet your objectives. Saving Changes...
Anonymous
I agree with the comments above. Know your data, understand your projects and create metrics that can be verified to ensure accuracy. Saving Changes...
Timothy KenneyProgram Manager| USAFBlack Hawk, Sd, United States
I always have to check my assumptions. I often find when directing my AI I assume it already knows what my intentions and needs are. It is not until I get the first output that I realize I forgot to add a bit of information it would have needed. Saving Changes...
A very good question and also difficult to answer as well. However you have to go to the basics and say as far as you are concerned, how well are you versed with the subject at hand ?. There are facts which the AI will generate and if you can verify these facts the more reliable the generated response will be. The fewer the facts then it means that the Generative AI response is far from meeting your original goals. Then it becomes very critical that you review the accuracy , relevancy and the alignment of the response to your original need. Unfortunately there are no clearly defined metrics that one can use a model to evaluate an AI generated response. So from my personal experience I basically restrict AI to an area where i have sound knowledge of , else it becomes almost impossible to verify details generated by an AI if you venture into unchartered territory. However with long usage and exposure your confidence also tend to increase as well. The best practice and protocol to follow would be to consult subject matter expects to validate the AI generated response before making critical decisions based on it to avoid any inherent associated risks which you might be not aware of.
I believe that iteration is crucial to get more precise results but at the end we may have to rely on the accuracy of the response. Do you think that consulting an expert about a portion of the AI answer (let's say 30%) would make you fill confortable of the overall result? Saving Changes...
"Humanity has advanced, when it has advanced, not because it has been sober, responsible and cautious, but because it has been playful, rebellious and immature."
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