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?
PM Consultant| CLOUD SAFE CO., LTD.New Taipei City, NWT, Taiwan
“Vision before Solution” for AI: make the decision explicit, ground answers in the system-of-record, and log everything so it’s reproducible. Low-risk = spot checks; high-risk = SME sign-off. Saving Changes...
Specific request and refinement by interaction using the correct database is the way to extract expected results. Tools are similar on this two criteria. No rocket science, ward work in my opinion. Saving Changes...
1. Be Clear and Specific in Your Input
Define your goal: What are you trying to achieve? (e.g., “Summarize this article for a presentation” vs. “Tell me what this article is about.”)
Include context: Mention relevant background, constraints, or preferences.
Use precise language: Avoid vague terms like “good” or “interesting” unless you define what they mean to you.
✅ 2. Validate the Output
Cross-check facts: Especially for data, statistics, or legal/medical info—verify with trusted sources.
Ask for sources or citations: If available, request references to support the AI’s claims.
Use multiple tools: Compare results from different AI systems or search engines when accuracy is critical.
🧠 3. Iterate and Refine
Review and adjust: If the first response isn’t quite right, clarify or rephrase your request.
Break down complex tasks: Tackle large goals in smaller steps to improve precision.
Ask follow-up questions: This helps guide the AI toward your intended outcome.
🛠️ 4. Use AI as a Collaborator, Not a Final Authority
Treat AI as a thinking partner—use it to brainstorm, draft, or analyze, but apply your own judgment.
Combine AI output with your domain expertise or human review, especially in professional or sensitive contexts.
🔐 5. Be Mindful of Privacy and Security
Avoid sharing personal, confidential, or sensitive data unless you're using a secure, trusted platform.
Understand how your data is used and stored by the AI system you're interacting with. Saving Changes...
1. Be Clear and Specific in Your Input
Define your goal: What are you trying to achieve? (e.g., “Summarize this article for a presentation” vs. “Tell me what this article is about.”)
Include context: Mention relevant background, constraints, or preferences.
Use precise language: Avoid vague terms like “good” or “interesting” unless you define what they mean to you.
✅ 2. Validate the Output
Cross-check facts: Especially for data, statistics, or legal/medical info—verify with trusted sources.
Ask for sources or citations: If available, request references to support the AI’s claims.
Use multiple tools: Compare results from different AI systems or search engines when accuracy is critical.
🧠 3. Iterate and Refine
Review and adjust: If the first response isn’t quite right, clarify or rephrase your request.
Break down complex tasks: Tackle large goals in smaller steps to improve precision.
Ask follow-up questions: This helps guide the AI toward your intended outcome.
🛠️ 4. Use AI as a Collaborator, Not a Final Authority
Treat AI as a thinking partner—use it to brainstorm, draft, or analyze, but apply your own judgment.
Combine AI output with your domain expertise or human review, especially in professional or sensitive contexts.
🔐 5. Be Mindful of Privacy and Security
Avoid sharing personal, confidential, or sensitive data unless you're using a secure, trusted platform.
Understand how your data is used and stored by the AI system you're interacting with. Saving Changes...
Erica ParkerResearch Associate I| DLHNC, United States
Continuously checking your output, build relevant checkpoints into your prompt, and providing the AI with similar examples. Saving Changes...
Erica ParkerResearch Associate I| DLHNC, United States
Saving Changes...
Erica ParkerResearch Associate I| DLHNC, United States
That's a very good question. In my response, I am assuming that the question refers to an LLM-based chatbot.
From my experience, the best results are achieved the more context I provide to the LLM. This means providing as much information as possible that describes both the project itself and the project context.
A second very important step is the quality of the request, also known as the prompt for the LLM. This is similar to human communication, where the quality of the question determines the quality of the answer. Therefore, a good prompt strategy is required, for example:
1. Data and context about the project
2. The goal of the request
3. The task that the LLM should fulfill
4. The format in which the output should be delivered.
In subsequent requests, it is possible to build on the context and results of the previous request. It is important that this process takes place within a chat, as otherwise the context is lost.
Yes, always check the quality of the work to make sure the chatbot is using up to date and relevant information. Saving Changes...
Joshua Anthony DatoTechnical Project Manager| Coral ActiveAustralia, Philippines
When using AI tools, I’ve learned that the best way to get accurate and relevant results is to start with a clear goal, ask focused questions, and always validate the output against trusted sources or your own expertise. AI can be incredibly helpful, but as project managers we still need to stay aware of the risks, like over-relying on automated suggestions, overlooking context that only humans can understand, or losing our ability to think critically and communicate clearly if we depend too much on prompts. At the same time, AI and good prompt engineering can elevate our work by helping us analyze information faster, prepare documentation more efficiently, and make better decisions with the data we have. Used thoughtfully, AI becomes a partner that boosts our value rather than replacing the judgement, empathy, and leadership that define strong project managers.
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