The AI Power Hour: Mastering AI Prompting for Efficiency and Success

Chad

Welcome, welcome, welcome, everyone, to the AI Power Hour! I’m Chad, the resident AI power user, and today we’re diving deep into the art of AI prompting. Whether you’re a tech enthusiast or a skeptical newcomer, this episode is packed with insights, tips, and a few laughs. And joining me is Karen, our AI skeptic extraordinaire. Karen, how are you doing today?

Karen

Oh, Chad, I'm doing great! But you know, I’m still a bit wary of AI. It’s like trying to get a teenager to do their homework—sometimes you just don’t know what you’re going to get. But I’m here to learn and maybe even have a little fun. So, let’s get started. What exactly is AI prompting anyway?

Chad

Great question, Karen! AI prompting is all about giving clear instructions to AI tools so they can generate the outputs you need. Think of it like writing a recipe for a chef. If you tell the chef, ‘Make a cake,’ they might make a vanilla cake, a chocolate cake, or even a sponge cake. But if you specify, ‘Make a three-layer chocolate cake with vanilla frosting and a raspberry filling,’ you’re much more likely to get exactly what you want. In the same way, a well-crafted prompt guides the AI to produce high-quality, relevant, and efficient results.

Karen

Hmm, that makes sense. But what happens when the chef, or in this case, the AI, decides to add some extra sprinkles or a weird flavor you didn’t ask for? How do we avoid that?

Chad

Ah, great point! That’s where the core principles of effective prompting come in. First, clarity is key. Avoid ambiguous terms and be as specific as possible. Second, provide context. Tell the AI who the audience is, what the tone should be, and any background information. Third, use examples. Show the AI what you’re looking for. Fourth, iterate. Refine your prompts based on the results you get. And finally, set rules. If you need the output to be concise, avoid jargon, or follow specific guidelines, make sure to state those rules upfront.

Karen

Umm, that sounds like a lot to keep in mind. But what about when you’re dealing with something more complex, like HR tasks? How do you ensure the AI doesn’t go off the rails when screening candidates?

Chad

HR tasks are a fantastic example! When you’re screening candidates, a good prompt might look something like this: ‘Analyze these 50 resumes for an Instructional Systems Designer (ISD) position. Identify candidates with experience in curriculum development, e-learning design, and adult learning principles, as well as a bachelor’s or master’s degree in instructional design, education, or a related field. Rank the top 10 candidates and provide a summary of their qualifications.’ This prompt is specific, it gives the AI clear criteria to follow, and it results in actionable insights.

Karen

But what if the AI starts ranking candidates based on their social media presence or something silly like that? How do we make sure it sticks to the important stuff?

Chad

That’s a common pitfall, Karen. You have to be very clear about what you’re looking for. For example, if you say, ‘Review these resumes,’ the AI might not know to focus on the relevant experience and qualifications. By specifying the exact criteria, you guide the AI to the right focus. And always double-check the results to make sure the AI hasn’t added its own flair. It’s like proofreading a friend’s essay—sometimes they add something you didn’t ask for.

Karen

Okay, I see. So, what about project management? I’ve heard some folks are using AI to automate status reports and predict risks. How do you make sure the AI doesn’t overpromise or underdeliver?

Chad

Project management is another area where AI can shine. A strong prompt could be: ‘Prioritize these 100 project tasks based on urgency, impact, and dependencies. Identify tasks that are critical for meeting the project deadline and recommend a task execution sequence.’ This way, the AI understands the importance of each task and can provide a structured, actionable plan. It’s like giving a GPS specific waypoints to follow instead of just saying, ‘Take me there.’

Karen

But what if the GPS starts suggesting shortcuts that lead to dead ends? I mean, have you ever had an AI tool that just didn’t get the project dependencies right?

Chad

Absolutely, Karen. It’s crucial to verify the AI’s outputs, especially for critical tasks. You can use historical data to cross-check the AI’s risk assessments and timelines. If the AI suggests a shortcut that seems off, dig into the details and ask it to explain its reasoning. For instance, if it says a task is low priority but you know it’s critical, ask, ‘Why do you think this task is less urgent?’ This helps you understand its logic and make informed adjustments.

Karen

Interesting. So, what about instructional design? I’ve seen some AI-generated lesson plans that look like they were written by a robot. How do you ensure the AI maintains the human touch?

Chad

That’s a great point, Karen. In instructional design, the AI needs to understand the learning objectives and the audience. A good prompt might be: ‘Assess this instructional design document for a 60-minute e-learning module on data privacy, checking for alignment of learning objectives with content, validity of formative assessments, and adherence to accessibility guidelines (WCAG 2.1).’ This ensures the AI reviews the document for pedagogical soundness and accessibility, which are crucial for effective learning. It’s like having a teaching assistant who knows exactly what to look for in a lesson plan.

Karen

Hmm, I can see how that would help. But what if the AI starts adding unnecessary pop quizzes or flashy animations that just distract from the content? How do we keep it on track?

Chad

You’re right, Karen. Overly flashy or irrelevant content can be a problem. When you’re generating instructional graphics, for example, a clear prompt would be: ‘Generate instructional graphics that illustrate the steps of an effective feedback process in a virtual training session. Use clean, professional visuals with minimal text to enhance learner comprehension and engagement while maintaining accessibility standards.’ This way, the AI knows to focus on clarity and relevance, rather than just making something look cool.

Karen

Umm, that’s a relief. But what about coding? I’ve got a friend who tried to use AI to generate a simple navbar, and it ended up with a bunch of security flaws. How do you ensure the AI doesn’t mess up the code?

Chad

That’s a classic example, Karen. When you’re generating code, you need to be very precise. A strong prompt could be: ‘Generate React code for a responsive navigation bar with a search function, styled using Material UI. Include comments explaining the purpose of each section of code.’ This ensures the AI knows exactly what you need, from the technology to the styling, and it also helps you understand the code better. It’s like asking a mechanic to fix your car and explain each step—they’re less likely to overlook something important.

Karen

But what if the AI starts writing comments in Shakespearean English or something equally bizarre? How do you keep it from getting too creative?

Chad

Haha, that’s a great tangent, Karen! You can set rules in your prompt to keep the AI grounded. For example, specify, ‘Keep comments concise and avoid jargon or overly creative language.’ If you’ve had a bad experience, share it with the AI and ask it to learn from it. Like, ‘In the past, you’ve written comments in Shakespearean English. Please avoid that and keep it straightforward.’ This helps the AI understand what works and what doesn’t.

Karen

Okay, that’s really helpful. What about graphic design? I once asked an AI to create visuals for a course and got back a bunch of abstract shapes that made no sense. How do you ensure the visuals are actually useful?

Chad

Graphic design is another area where specificity is crucial. A good prompt might be: ‘Generate instructional graphics that illustrate the steps of an effective feedback process in a virtual training session. Use clean, professional visuals with minimal text to enhance learner comprehension and engagement while maintaining accessibility standards.’ This way, the AI knows to create visuals that are not only visually appealing but also functional and accessible. It’s like telling a painter exactly what you want in a mural—no abstract art unless that’s what you’re going for!

Karen

Umm, I can see how that would help. But what if the AI decides to add a unicorn or a dragon to the feedback process graphic? How do you keep it from getting too whimsical?

Chad

Ah, the classic unicorn problem! You can set limits in your prompt. For example, ‘Avoid using any abstract or fantastical elements. Stick to professional, clean designs.’ If the AI still goes off the rails, don’t hesitate to give it a gentle nudge back. It’s like training a puppy—consistent feedback helps it learn what’s appropriate and what’s not.

Karen

That’s a great analogy, Chad. But what about proposal writing? I’ve heard some horror stories about AI-generated proposals that were riddled with inaccuracies. How do you make sure the AI gets it right?

Chad

Proposal writing is another area where AI can be a game-changer, but it requires careful prompting. A solid prompt might be: ‘Review a proposal draft, focusing on the clarity and accuracy of the budget narrative, compliance with RFP requirements, and the strength of the proposed evaluation plan.’ This ensures the AI fact-checks the necessary sections and adheres to the RFP guidelines. It’s like having a proofreader who knows exactly what to look for in a business document.

Karen

Hmm, I can see why that would be important. But what if the AI starts suggesting evaluation plans that are totally unrealistic? How do you keep it from dreaming up something that’s impossible to implement?

Chad

That’s a great question, Karen. You can add constraints to your prompt, like ‘Ensure the evaluation plan is feasible within the given budget and timeline.’ This helps the AI stay within the bounds of reality. And always review the AI’s suggestions with a critical eye. If something seems off, ask for more details or alternatives. It’s like working with a consultant—sometimes you need to ask follow-up questions to get the best results.

Karen

Umm, that makes a lot of sense. But what about this G.O.A.L.S. framework you mentioned? How does that fit into all of this?

Chad

The G.O.A.L.S. framework is a fantastic tool for crafting effective prompts. G stands for Give AI a Role, like ‘Act as an instructional designer specializing in compliance.’ O is Outline the Task, clearly stating what the AI should do. A is Add Context and Details, providing background information and specific criteria. L is List the Desired Output, specifying the response format. And S is Set Limits or Rules, adding constraints to guide the AI. By following these steps, you can create prompts that get you exactly what you need, every time.

Karen

Hmm, that sounds really useful. But what if the AI starts acting like a know-it-all and ignores the rules? How do you keep it in check?

Chad

That’s a common issue, Karen. If the AI starts acting up, you can use the iteration principle. Refine your prompts based on the results you get. For example, if it’s not following the rules, you might say, ‘Revise this with more actionable steps and stick to the budget constraints.’ This helps the AI understand that you’re the one in charge and it needs to listen to your instructions. It’s like telling a stubborn assistant, ‘No, we’re doing it this way.’

Karen

Umm, that’s a relief. But what about security and ethical considerations? I’ve heard some scary stories about people accidentally sharing sensitive data with AI tools. How do we avoid that?

Chad

That’s a critical point, Karen. Always ensure that the AI platform you’re using is approved for workplace use. Never input company or customer proprietary, confidential, or personally identifiable information (PII). Assume that data shared with AI may be stored or used in ways you can’t control. And remember, Controlled Unclassified Information (CUI) should never be entered into any AI tool. If you’re unsure, always consult your project manager or IT/security team. It’s like keeping a locked file cabinet for all your important documents—better safe than sorry.

Karen

Hmm, that’s really important to keep in mind. But what if you’re working on a project and the AI tool you’ve been using isn’t approved? How do you handle that without panicking?

Chad

If you find yourself in that situation, the first step is to stop using the unapproved tool immediately. Reach out to your project manager or IT team to get the vetting process started. In the meantime, you can use approved tools or manual methods to continue your work. It’s like realizing you’ve been using the wrong key to lock your door—switch to a trusted method until you can get the right key. And always follow company policies to protect sensitive data. The last thing you want is a data breach or security risk.

Karen

Umm, that’s really reassuring. So, what’s the biggest takeaway from all this? How do we ensure we’re using AI effectively and responsibly?

Chad

The biggest takeaway, Karen, is that clarity, context, and continuous refinement are key. Use the G.O.A.L.S. framework to create structured, effective prompts. Always double-check the AI’s outputs, and if you encounter any issues, don’t hesitate to iterate and improve. And most importantly, never share sensitive information with unapproved AI tools. By following these guidelines, you can harness the power of AI to boost your efficiency and effectiveness, all while staying secure and ethical. Thanks for joining me today, Karen, and to all our listeners out there. Stay tuned for more AI adventures!

Karen

Thanks, Chad! It’s been a lot of fun, and I’m definitely feeling more confident about using AI now. And who knows, maybe next time I’ll ask the AI to design a unicorn-themed feedback process, just for kicks! But seriously, thanks for all the great tips and insights. See you next time!