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When using AI systems, what are some best practices for ensuring the results you receive are accurate, relevant, and aligned with your original goals?

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Sarah Philbrick
PMI Team Member
Director, Learning Design & Development| PMI Asheville, 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?
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Joseph Jones Falls Church, VA, United States
Clearly define your goals and input accurate, specific data.
Use reliable, well-trained AI systems.
Cross-check results with trusted sources.
Regularly update and validate the AI model.
Monitor and refine AI outputs to ensure alignment.
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Mohammed Azher India
reviewing and validating the results and suggesting improvements with prompts will provide better results.
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Jessica Laughlin Alexandria, VA, United States
I like to stick to topics that I have a decent understanding of. For those I do not, asking the AI to provide links to sources is always required.
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SAIDA NJIM PM Consultant| FCS Nouvelle Medina, Tunisia
is important to :
Consider that AI is limited by the lack of human interaction
and it is preferable to structure your requests to achieve your objectives.
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Mahesh Kumar Chennai, TN, India
Validation checks to be added in the prompt so that we will get the precise output from the system.
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Taisuke Iwamoto TMEIC Corporation Tokyo, Japan
In creative and novel projects, it's often hard to judge AI outputs. So I have to treat them as ideas from a single team member or an assistant to be checked by human. If consistent answers emerge from multiple prompts, I may place some trust in them. For less innovative projects, I use standardized input formats combined with Lessons&Learned, but whether this is the best or most comprehensive approach remains open to debate.
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Valerie Brown Program Management| Anthem Inc. Fort Myers, FL, United States

By defining clear objectives and using the CREATE formula, you can enhance the accuracy, relevance, and alignment of AI-generated results with your original goals. Clear objectives, high-quality data, understanding your AI model, continuous monitoring, feedback loops, scenario testing, adherence to ethical guidelines, and collaboration with experts are key factors that contribute to the effective and responsible use of AI systems.



Implementing these practices will not only improve the reliability of your AI-driven outcomes but also help you leverage AI’s full potential in achieving your desired objectives.
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Ruba Abu Subaih Training and Development Specialist, Project Manager Houston, United States
From my experience, I have achieved the best results by applying the CREATE formula and following a structured approach. This includes providing detailed context to the LLM, clearly setting the tone, and being specific with requests. Additionally, adopting a human-in-the-loop approach—where I continuously evaluate and iterate on the generated outputs while requesting additional resources—has been critical to refining results. These steps have consistently enabled me to maximize the effectiveness and accuracy of the outputs.
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James Tanner Vice President| TurningPoint Silver Spring, Md, United States
Validate using an Analysis prompt and also asking AI tool for the specific references lends to the credibility. Always review as much as humanly possible. Refine.Refine.Refine.
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Shauna VanderHoek Sarasota, Fl, United States
Jun 12, 2024 1:31 AM
Replying to Jabin Geevarghese George
...

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.

Jabin, I love your analogy. I was thinking how prompting is much like the need to be clear and concise with a new-hire who has no idea what you are looking for. 😉
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