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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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Manu Srivastava

Best Practices for AI-Generated Results as per my opinion:



Be Specific & Contextual: Clearly define your goal, constraints, and desired format (e.g., "Summarize this research in 3 bullet points for executives, excluding technical jargon").



Iterate & Refine: Test prompts, adjust based on outputs, and use feedback loops (e.g., "Revise with more data-driven examples") to improve accuracy.



Validate & Cross-Check: Verify AI responses against trusted sources and refine prompts to reduce ambiguities (e.g., "Compare with industry benchmarks").
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Dorcas Oforiwaa Sakyi Accra, Ghana
I use a human in the loop approach to verify the outcomes I receive from GenAI tools. This helps to ensure the results I receive are accurate, relevant, and aligned with my goals.
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Syed Imran Ulhaq Jeddah, 02, Saudi Arabia
while using AI system, to get accurate, relevant and your goal aligned responses or results , it require a mix and match of critical thinking, clear and to the point prompt, and last but no least verification process.
Some best practices should include :
- clear and specific prompt
- validate and cross checking of information recieved
- keep AI limitations in mind
- Ensuring compliance and risk
- Audit and monitoring of AI outputs regularly
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Ododoade Adewuyi Analyst| CW Real Estate portland, ME, United States
Jun 07, 2024 9:24 AM
Replying to Sergio Luis Conte
...
AI is a broader term. Generative AI is just an ancient model but everything "explode" when Google published the new architecture called transformer in 2017. So, with that said, take into account that generative AI is just "predictive test with steroids" just simplifying the model. With that said, two key points has to be taking into account when somebody works with AI: 1-human in the loop. 2-AI without Data (today called data science discipline or big data or whatever) is the same thing that live without oxygen. Talking about generative AI all related to technology has almost not impact with relation to all related to non-technological roles and activities. What you stated about accuracy and things like that are easy to implement because there are a lot inside disciplines like statistics. Most of them to make things "a priori" to prevent instead of cure. Few organizations taking into account that when generative AI environments are put in place almost a new business unit has to be created where roles like lawyers, linguistic, diversity and inclusion specialist must be hire to help on put it in place.
In my experience using tools like Salesforce and Qualtrics in project settings, I’ve found that getting accurate and relevant results from AI really depends on how clearly you frame your goals from the start. I always try to be specific with my inputs and make sure they align with what I actually need from the system, whether that’s analyzing feedback or streamlining reports. Once I get results, I don’t just take them at face value; I double-check them against what I already know or what the project requires. If something doesn’t feel right, I tweak the prompt or ask follow-up questions until it makes sense. And honestly, getting a second opinion from a teammate has helped a lot too, sometimes another set of eyes can catch what you might miss. So for me, it’s a mix of clarity, iteration, and not relying on AI to do all the thinking for you.
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Kylie Collins Relationship Banker| jpmorgan chase Houston, Tx, United States
Using the CREATE model will elevate and give more tailored results
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Anonymous
Informative!
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Chioma Ogbuokiri PM I| Intellibridge Ellicott City, United States
When using AI systems; ensure data integrity, verify the data is accurate, thoroughly test the AI system and comply with existing regulations on data integrity and ethics
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Trayana Damyanova Transition Manager| IBM Krakow, Malopolska, Poland

To ensure the results from AI systems are accurate, relevant, and aligned with original goals, consider the following best practices:



Clearly define the problem and objectives: Clearly articulate the problem you want to solve and the desired outcomes. This will help guide the AI system and ensure its output is relevant to your needs.



Select the appropriate AI tool: Choose an AI system that is well-suited to your specific problem and industry. Research and compare different AI tools to find the one that best fits your requirements.



Provide high-quality input data: Feed the AI system with accurate, relevant, and diverse data. The quality of input data directly impacts the quality of the output.



Validate and verify results: Cross-check the AI system's output with other sources or methods to ensure its accuracy. This step is crucial for maintaining trust in the AI system and its results.



Continuously monitor and update: Regularly review the AI system's performance and update its parameters or algorithms as needed. This will help maintain its accuracy and relevance over time.



Collaborate with domain experts: Work with subject matter experts to interpret and validate the AI system's results. Their insights can help ensure the output aligns with your original goals and industry-specific requirements.



Foster a culture of transparency and ethics: Encourage open communication about the AI system's limitations and potential biases. This will help build trust in the system and promote responsible use of AI.



Stay informed about advancements: Keep up-to-date with the latest developments in AI technology and best practices. This will enable you to make informed decisions about adopting new tools and techniques to improve your AI system's performance.



Implement a feedback loop: Establish a process for collecting user feedback and incorporating it into the AI system's development and improvement. This will help ensure the system remains aligned with user needs and expectations.



Ensure compliance with regulations: Be aware of and adhere to any relevant regulations governing AI use in your industry. This will help maintain the integrity and trustworthiness of your AI system.



By following these best practices, you can maximize the accuracy, relevance, and alignment of AI system results with your original goals.
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AARTHI RAGHAVENDRAN CHENNAI, TN, India
Setting the context in detail, providing specific instructions with examples, using the RTF or CREATE patterns, evaluating the output for accuracy are some of the best practices I suggest for using Gen AI chatbots.
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Kayode Momoh Chief Operating Officer| Kaltani Nigeria Limited Lagos, Nigeria
I intend to use the CREATE formular to commence my prompting journey and keep refining until I become very adapt at prompting.
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