Project Management

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

Artificial Intelligence

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?

Posted: Jun 6, 2024 1:53 PM

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RAJESH VADADI Lebanon, Tn, United States

I also follow an iterative approach by reviewing AI responses, validating critical information against trusted sources, and refining prompts based on the results. Human judgment and subject matter expert feedback remain essential, especially when making project decisions.

As project requirements evolve, continuously updating the AI with new information and constraints helps keep its responses aligned with changing objectives. Ultimately, I see AI as a valuable tool to support decision-making, not replace human expertise and accountability.

Posted: Oct 26, 2026 11:43 AM

RAJESH VADADI Lebanon, Tn, United States

To ensure AI-generated results are accurate, relevant, and aligned with project goals, I believe it is important to start with clear prompts that define the objectives, context, expectations, and desired output.
I also follow an iterative approach by reviewing AI responses, validating critical information against trusted sources, and refining prompts based on the results. Human judgment and subject matter expert feedback remain essential, especially when making project decisions.
As project requirements evolve, continuously updating the AI with new information and constraints helps keep its responses aligned with changing objectives. Ultimately, I see AI as a valuable tool to support decision-making, not replace human expertise and accountability.

Posted: Oct 26, 2026 11:44 AM

Sushma Begum Shaik India

Posted: Oct 27, 2026 11:04 AM

David Van Dyke IT Project Manager| Administrative Office of the United States Courts Washington, Dc, United States

Jun 11, 2024 2:25 PM

Replying to Melissa Stockbridge

...

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.

Absolutely agree with the points you were making and utilizing our typical project management approach of, "breaking down big problems can be better refined by chunking the whole into natural sections".

Posted: Oct 28, 2026 11:50 AM

Syed Atif Masroor Project Management| RED SEA GLOBAL Abu Dhabi, AZ, United Arab Emirates

From a PMIS perspective, I approach AI in much the same way we manage project information: good output depends on controlled input, clear requirements, validation and accountability.
I normally define the required outcome first, provide authoritative project context, specify assumptions and constraints, and then validate the response against the PMIS, controlled documents, procedures or contractual source.
I also deliberately ask AI to challenge the result: What assumptions were made? What information is missing? What contradicts the conclusion? What evidence would change the answer?
The biggest lesson for me is that AI should not become another uncontrolled source of project information. Used properly, AI can significantly improve analysis, reporting and decision support, but the process still needs traceability, governance and appropriate human review.
That is also why I see PMIS and Information Management professionals having an important role in enterprise AI adoption. Many of the controls AI needs already exist within our discipline.

Posted: Oct 29, 2026 7:11 AM

Karthikeyan Senthamilselvan India

Clearly define your specific objectives, expectations, and success criteria before writing a prompt. Provide rich context by including background documents, examples, or a clear persona to guide the model's output. Employ structured prompting techniques, such as chain-of-thought reasoning or lower temperature settings, to enhance logical accuracy. Iterate on responses by asking the system to refine its answers or by feeding back constructive corrections. Always maintain human oversight to cross-reference facts, verify sources, and ensure alignment with your original goals.

Posted: Oct 29, 2026 2:10 PM