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Everyday AI Made Simple – AI for Everyday Tasks is your friendly guide to getting useful, not vague, answers from AI. Each episode shows you exactly what to type—with plain-English, copy-ready prompts you can use for real life: budgeting and bill-balancing, meal and grocery planning, decluttering and home routines, travel planning, wellness tracking, email writing, and more.
You’ll learn the three essentials of great prompts (be specific, add context, assign a role) plus easy upgrades like formats, guardrails (tone, length, “no jargon”), and iterative follow-ups that turn “hmm” into “heck yes.” No tech-speak, no eye-glaze—just practical steps so you feel confident and in control.
If you’re AI-curious, and short on time, this show hands you the exact words to use—so you can save your brain for the good stuff. New episodes keep it short, actionable, and judgment-free. Think: your smartest friend, but with prompts.
Blog: https://everydayaimadesimple.ai/blog
Free custom GPTs: https://everydayaimadesimple.ai
Some research and production steps may use AI tools. All content is reviewed and approved by humans before publishing.
00:00:00 Speaker 1
All right, welcome back to the deep dive. So you clicked on this deep dive, and I bet it's because you felt that universal frustration, asking a really powerful AI, like ChatGPT or Gemini, a question, expecting something amazing, and you just get back this aggressively mediocre answer.
00:00:20 Speaker 2
Oh, absolutely. It's that instant letdown, isn't it? We've all been there. You go to these incredible tools, these large language models, thinking you're going to get some bespoke, brilliant insight.
00:00:31 Speaker 1
Right, actionable stuff.
00:00:32 Speaker 2
And instead, it's just so generic. So surface level, it almost makes you think maybe this whole AI thing is a bit overhyped.
00:00:39 Speaker 1
I feel that constantly. It's like I'm expecting this genius consultant who knows absolutely everything. And what I get feels more like a chatbot just spitting back the most obvious points from the first. Few search results. It's a real time waster.
00:00:53 Speaker 2
It really is. And you start to doubt the whole productivity revolution promise. Yeah. But here's the thing, and this is really the secret, the key to flipping. that switch from being just a casual user to someone who gets real value. The difference between that vague, frustrating answer and something genuinely brilliant, something tailored, it's almost 100% down to the quality of the question you asked in the first place.
00:01:14 Speaker 1
Ah, okay. So it's on us then.
00:01:16 Speaker 2
Pretty much. It's that classic computer science idea, right? Garbage in, garbage out. Same principle applies here. Just turbocharged.
00:01:23 Speaker 1
Okay, let's unpack that. Let's break this frustration loop.
00:01:26 Speaker 2
Okay.
00:01:26 Speaker 1
So our mission today in this deep dive is basically to give you that shortcut to brilliance. We're going deep into mastering the art of prompting.
00:01:35 Speaker 2
Yeah, how to talk to the AI.
00:01:37 Speaker 1
How to talk to it effectively.
00:01:38 Speaker 2
Yeah.
00:01:38 Speaker 1
How to communicate what you actually need, your constraints, your goals, in a way the AI can really grab onto and use.
00:01:45 Speaker 2
And the aim here isn't just, you know, slightly better search results. That's thinking too small.
00:01:49 Speaker 1
Right.
00:01:50 Speaker 2
The goal is to shift you from getting these basic summaries to actually using the AI as a specialist. Like a task-focused assistant.
00:01:58 Speaker 1
A powerful assistant.
00:01:59 Speaker 2
Exactly. So we're going to unlock what we're calling the smart friend secret and give you basically a new toolkit, three core rules to write prompts that get you consistently better, often professional growth results.
00:02:13 Speaker 1
OK, I like the sound of that. A toolkit.
00:02:15 Speaker 2
OK.
00:02:15 Speaker 1
So let's start with the foundation, this sort of philosophical shift you mentioned, the smart friend secret philosophy.
00:02:21 Speaker 2
Right. So the mind trick, the shift you need to make, it sounds simple, but it's actually pretty profound. OK. You have to stop thinking of the A.I. as just this giant faceless database or like a search engine, that old model. It just encourages you to be vague.
00:02:37 Speaker 1
Right. Like typing keywords into Google.
00:02:38 Speaker 2
Exactly. Instead, you need to start treating it like a really smart friend, someone who's incredibly knowledgeable, very capable, but still needs some help from you.
00:02:47 Speaker 1
OK, a smart friend. I get that.
00:02:49 Speaker 2
Yeah.
00:02:49 Speaker 1
But even my smartest friends, if I just ask them something super broad, they can't really give me a great answer, can they.
00:02:55 Speaker 2
Precisely. That's the key. Even a genius friend needs details. They need context.
00:03:00 Speaker 1
I'm going to walk up to my smartest colleague and just say, hey, tell me about the future of finance.
00:03:04 Speaker 2
Yeah, what are they going to say? They'll probably laugh or give you a one-sentence non-answer.
00:03:08 Speaker 1
They need to know what I'm really asking. Am I talking about crypto, fintech, regulations.
00:03:14 Speaker 2
Exactly. A smart friend isn't psychic. They need you to be specific. They need the background info, the context, and crucially, they need to know what hat you want them to wear, what role you need them to play to make their knowledge relevant to you.
00:03:29 Speaker 1
Ah, okay. So if I don't give them that.
00:03:31 Speaker 2
If you fail on that, they'll just default to the safest, broadest possible answer, which is almost always the most useless answer. They just kind of retreat to the average of everything they know.
00:03:41 Speaker 1
Let's make that super concrete. That travel advice analogy is good.
00:03:45 Speaker 2
Yeah. Think about asking a friend about a trip.
00:03:47 Speaker 1
Okay.
00:03:48 Speaker 2
If you just text your friend, hey, tell me about Kyoto, what does that even mean.
00:03:51 Speaker 1
Right. History, food, best temples, nightlife, where to find cool vintage shops.
00:03:58 Speaker 2
Exactly. They have no idea. Are you going for a weekend, a month? Are you on a tight budget? Are you into modern art or ancient history? The answer they give back will be so broad, it's basically useless.
00:04:10 Speaker 1
Okay, but compare that to asking, uh... Okay, listen. I'm heading to Kyoto for 10 days in March. I'm planning this metography project, focusing specifically on traditional architecture and, like, seasonal nature stuff. Maybe cherry blossoms if they're early.
00:04:24 Speaker 2
Okay, getting specific.
00:04:26 Speaker 1
My budget is roughly $300 per day for everything. Can you maybe sketch out a three-day itinerary for me? Big constraint. I need locations easily reachable by bus. And ideally, places that aren't totally swamped with photographers already kind of looking for those less common shots.
00:04:43 Speaker 2
Now, that is a prompt. See the difference.
00:04:45 Speaker 1
Huge difference.
00:04:46 Speaker 2
Your friend now knows the duration, 10 days total, three days for this plan, the timing, March implies weather events, your specific interest, photography, architecture, nature, your goal, less common shots, your budget, $300 a day, and your logistics.
00:05:00 Speaker 1
constraint, bus access. They can actually give me a useful plan now, something I can literally.
00:05:04 Speaker 2
follow. Exactly. A perfect, actionable plan. And the AI works on the exact same logic. The quality of the itinerary or whatever output you want, it's directly tied to how many and how well you define those constraints in your request. Okay. So this smart friend idea helps frame it.
00:05:22 Speaker 1
And it leads us to those three core rules you mentioned, the toolkit. Yep. The toolkit. These.
00:05:27 Speaker 2
three simple rules are the bedrock. Get these right. And honestly, your results will improve dramatically. Should we list them out quickly before we dive into each one? Yeah, let's do a quick rundown. Okay. Rule one, be specific, not vague. Got it. Rule two, give it context. The why behind the what. Makes sense. And rule three, tell it the role you want it to play. Cast the AI.
00:05:48 Speaker 1
Okay. Specificity, context, role. Let's dedicate the time now. Rule number one, be specific, not vague. You said this is the foundation. Absolutely. This is the big one.
00:05:58 Speaker 2
The absolute foundation. If you get this wrong, The rest doesn't matter as much.
00:06:02 Speaker 1
Why is it so fundamental.
00:06:03 Speaker 2
Because if the AI doesn't know precisely what target you're aiming for, it tries to cover all the possible targets. It sprays information everywhere.
00:06:12 Speaker 1
And you end up nowhere useful.
00:06:14 Speaker 2
Exactly. You just get noise. So you must eliminate vagueness. Vague questions, I promise you, will always, always yield vague, time-wasting, unhelpful answers.
00:06:27 Speaker 1
Okay, but let me push back slightly here. Play devil's advocate for a second. I sometimes hear people say, look, if I have to spend ages writing this super-specific, detailed prompt, isn't that just swapping one time-consuming task, figuring out the answer myself for another time-consuming task.
00:06:43 Speaker 2
That's a really fair question. Where's the efficiency gain? The gain comes from slashing the iteration time. Think about it. When you're vague, the AI gives you that big, generic blob of text. Then you have to spend time reading it, figuring out what's wrong or missing, and then writing follow-up prompts, make it shorter, focus on this aspect, change the topic. Tone. You might go back and forth five, six, seven times.
00:07:03 Speaker 1
Okay, I see where you're going.
00:07:04 Speaker 2
But when you're specific up front, you're essentially pre-filtering. You're telling the LLM, ignore all that other stuff. Just focus here. It drastically narrows the search space for the model from the get-go.
00:07:15 Speaker 1
So it finds the right path faster.
00:07:17 Speaker 2
Much faster. And more accurately, it's less likely to just make stuff up, you know, hallucinate because you've constrained its options. So you get a usable output much quicker, often on the first try. It saves you all that back and forth, that cognitive load of correcting it.
00:07:32 Speaker 1
Okay, that makes sense. It's front-loading the effort for a bigger payoff. Specificity is a focusing lens. Let's move beyond the simple, tell me about exercise example we touched on. That's a bit too basic. How about something with real professional value.
00:07:47 Speaker 2
Okay, good idea. Let's take a high-value business task. Say you need to generate a framework for a competitive analysis, but for a really specific niche B2B market.
00:07:58 Speaker 1
Okay. Something I might actually need to do at work.
00:08:00 Speaker 2
Exactly. So the bad, vague prompt would be something like, write a competitive analysis for my company.
00:08:06 Speaker 1
Right. And what would I get back.
00:08:08 Speaker 2
You'd get a textbook definition, a boilerplate template explaining SWOT analysis, maybe Porter's five forces.
00:08:15 Speaker 1
Stuff I already know. Yeah. Intellectually correct, maybe, but totally useless for my specific situation. It applies to everyone and therefore no one.
00:08:24 Speaker 2
Precisely. Now let's craft a specific prompt for the same goal. Okay. Act as a market analyst specializing in sustainable packaging. We'll get to roles later, but let's add it now. Generate a framework for a competitive landscape analysis. Focus specifically on the U.S. sustainable seafood packaging market.
00:08:43 Speaker 1
Okay. Getting very niche.
00:08:44 Speaker 2
The analysis needs to identify the key competitors who are actually based in the Pacific Northwest.
00:08:49 Speaker 1
Geography constraint.
00:08:50 Speaker 2
Assess their primary growth factors, specifically looking at data or announcements from Q4 2023.
00:08:56 Speaker 1
Time constraint. Very specific.
00:08:57 Speaker 2
And structure. Structure the final output into three. main sections. Section one on material science innovations they're using, section two giving a logistical efficiency score, let's say, on a scale of one to ten, and section three detailing their stated organizational sustainability commitments. Wow, okay, very structured output. And one final thing, present all of this comparative data in a markdown table format. Okay, that's night and day.
00:09:21 Speaker 1
compared to write a competitive analysis. Look at the layers of constraint. You've given it the.
00:09:25 Speaker 2
specific industry, sustainable seafood packaging, the geography, US, PNW, the time frame, Q4 2023, the exact deliverable structure, those three analytical sections, and the specific output format, the table. What does that force the AI to do? It forces it to stop being a generic essay writer. It has to become a specialized market researcher for this task. It can't just waffle, it has to go find, or simulate finding, specific data points. It's about materials, logistics, sustainability statements for those specific companies in that.
00:10:00 Speaker 2
region.
00:10:00 Speaker 1
And then organize it exactly how I asked.
00:10:03 Speaker 2
Exactly. It has to filter its vast knowledge base through all those constraints simultaneously. That's the power of specificity. It transforms the task from an academic exercise into creating a professional, actionable, deliverable.
00:10:15 Speaker 1
That leap from just retrieving general knowledge to actually solving a problem within tight constraints, that feels like the core benefit here. You're telling it, forget everything else you know for a moment. Just focus on the tiny intersection of these five specific requirements.
00:10:31 Speaker 2
You nailed it. That's the essence of rule one. Specificity is the foundation. It tells the AI what you want it to do in detail.
00:10:39 Speaker 1
Okay. Foundation laid. So once we've nailed the what, we need rule two, which you said is the complement. Give context.
00:10:45 Speaker 2
Exactly. If specificity is the what, context is the why, and often the who and the how.
00:10:49 Speaker 1
The why. Okay.
00:10:51 Speaker 2
Yeah.
00:10:51 Speaker 1
How does that differ from specificity.
00:10:53 Speaker 2
Specificity defines the task parameters. Context provides the crucial background information, the surrounding situation. It's what really shapes the appropriateness and usefulness of the answer for your specific situation. This is where, honestly, a lot of the magic happens.
00:11:09 Speaker 1
So context makes it relevant to me personally or my specific goal.
00:11:13 Speaker 2
Precisely. Context tells the AI why you're asking and that why dramatically influences things like the right tone to use, the level of detail needed, maybe even the risk tolerance in the answer.
00:11:25 Speaker 1
Can you give an example.
00:11:26 Speaker 2
Sure. Imagine you ask the AI to summarize some complex new government regulations. The summary itself should be factually accurate, right? Yeah. But the way it's summarized should change drastically depending on the context. Is the context, summarize this for our internal legal team to review potential compliance gaps? Or is the context, summarize this for a public press release aimed at reassuring our customers.
00:11:51 Speaker 1
Ah, okay. Totally different outputs needed.
00:11:54 Speaker 2
Yeah.
00:11:54 Speaker 1
One is about risk identification. The other is about public. Relations and reassurance.
00:11:58 Speaker 2
Exactly. The core facts might be the same. But the framing, the emphasis, the language, the level of technical detail, all of that is dictated by the context you provide. The why changes everything.
00:12:08 Speaker 1
Let's take another complex example. Maybe not just a simple email, but something higher stakes, like summarizing notes from a really important personnel performance review.
00:12:17 Speaker 2
Okay, great example. High stakes.
00:12:19 Speaker 1
The useless context-free prompt would just be, summarize this performance review document I've uploaded.
00:12:25 Speaker 2
And what would you likely get.
00:12:26 Speaker 1
Probably just a dry, neutral summary. It might just list the sections, strengths, weaknesses, goals. It wouldn't be very helpful for actually doing anything with it.
00:12:34 Speaker 2
Right, it just mirrors the structure. Now, let's inject context. First, who is this summary actually for.
00:12:41 Speaker 1
This summary is for the executive leadership board, an important audience. They literally have only three minutes to read this during the budget meeting.
00:12:49 Speaker 2
Okay, time pressure. Brevity is key.
00:12:51 Speaker 1
Next, what is the specific goal of this summary? The primary goal here is to strongly justify a significant budget. Increase request for this.
00:13:01 Speaker 2
Ah, okay, so it's not just a summary. It's a justification document. Persuasion is needed.
00:13:07 Speaker 1
Exactly. And finally, what tone is required? The tone needs to be overwhelmingly positive and motivational. Focus entirely on the demonstrated high-leverage accomplishments and future potential. Strategically minimize any mention of minor areas for development or frame them as already addressed. Wow. Okay, so with that context, the AI's task completely transforms, doesn't it.
00:13:30 Speaker 2
Completely. It's no longer just summarizing. It's crafting a persuasive argument. It needs to act almost like your chief of staff, understanding the political landscape of the meeting.
00:13:40 Speaker 1
It needs to know which facts to highlight, which to downplay.
00:13:44 Speaker 2
Precisely. It needs to structure the narrative to achieve that specific budgetary outcome. Without the context, maybe it gives equal weight to the employee hitting 200% of their sales target and the fact they were occasionally late for meetings.
00:13:58 Speaker 1
Right, which would kill the budget request.
00:13:59 Speaker 2
But with the context... The AI understands, okay, the sales figures are the headline. Drive that home. Frame the lateness, if mentioned at all, as a minor historical issue that's already resolved. The context defines the entire strategic purpose and shapes the output accordingly.
00:14:14 Speaker 1
I actually had an experience like this recently. I asked an LLM to help draft a new remote work policy document. And the first draft came back incredibly rigid. Super legalistic, very corporate, kind of draconian almost.
00:14:28 Speaker 2
Yeah, they can default to that very formal, risk-averse tone sometimes.
00:14:32 Speaker 1
And I was frustrated. Then I realized I hadn't given it any context about our company culture. So I went back and added, okay, rewrite this. But understand, this policy is for a small, highly flexible tech startup environment. Trust is our default setting. Compliance needs to feel really light touch and enabling, not restrictive.
00:14:52 Speaker 2
Ah, that's gold. What happened.
00:14:54 Speaker 1
The second draft was completely different. Much more collaborative. Focused on guidelines rather than rigid rules. use totally different language. It actually felt like us.
00:15:03 Speaker 2
See, you basically told it, start acting like a corporate lawyer for a massive bank. Start acting like an HR person in a fast-moving startup. The context shifted its entire underlying philosophy for the task.
00:15:15 Speaker 1
It really did. So, okay, we've got rule one, be specific the what. Rule two, give context the why, the who, the surrounding situation. That brings us to rule three, which you hinted might be the most impactful. Tell it the role you want it to play.
00:15:28 Speaker 2
Yes, this one. This is often the total game changer. For many people I talk to, this is where they have the biggest aha moment. It feels almost like a cheat code sometimes.
00:15:36 Speaker 1
A cheat code, how so.
00:15:38 Speaker 2
Because you are literally explicitly casting the AI. You're giving it a job title, a persona, right at the start of your prompt, before you even state the task. And it's incredibly effective.
00:15:48 Speaker 1
So it's more than just context. How does act as a CARA differ from providing context.
00:15:54 Speaker 2
It's like applying a system-level filter before the context even matters. Think about it like this. Some AIs have what's called a system prompt. It's like a hidden instruction given to the AI before it even sees your prompt, setting the general rules, the personality.
00:16:08 Speaker 1
Okay, like telling ChatGPT you are a helpful assistant.
00:16:11 Speaker 2
Exactly. When you start your prompt with act as a specific role, like act as a skeptical financial risk analyst or act as an enthusiastic travel blogger, you're essentially hijacking or overriding part of that default system prompt. You're forcing the AI to adopt a much more specific, constrained operational mode right from the start.
00:16:32 Speaker 1
I see. So it changes its entire way of thinking before it even processes my specific request in context.
00:16:37 Speaker 2
Precisely. It instantly shifts the AI's whole mindset, its perspective. It stops being this generic know-it-all machine.
00:16:44 Speaker 1
And becomes a specialist.
00:16:45 Speaker 2
Yes. It adopts the vocabulary, the priorities, the inherent biases, even the likely knowledge depth of that specific role. General knowledge is often wide but shallow. The knowledge accessed via role player becomes narrow but potentially. much deeper and more relevant.
00:17:02 Speaker 1
Okay, let's see it in action. Give me an example. Maybe information retrieval again.
00:17:06 Speaker 2
Okay, let's say you prompt, quite simply, tell me about Rome.
00:17:11 Speaker 1
Vague, but okay, what do I get.
00:17:13 Speaker 2
You'll probably get a decent encyclopedia-style summary. History, Colosseum, Vatican City, maybe some famous emperors, facts.
00:17:21 Speaker 1
Dry, academic, maybe a bit boring.
00:17:23 Speaker 2
Probably. Now let's apply a role. Start with, act as an experienced local tour guide specializing in short trips. Then add specificity and context. Tell me about Rome. I only have two full days there, and I absolutely love ancient history, but also really good coffee shops.
00:17:39 Speaker 1
Okay, tour guide, two days, history, and coffee.
00:17:41 Speaker 2
What do you think the output looks like now.
00:17:43 Speaker 1
It's not going to be a list of emperors, is it? It's going to be a schedule.
00:17:47 Speaker 2
Exactly. It's going to be a practical, maybe even hour-by-hour itinerary. Okay, day one, start early at the Colosseum, grab an amazing espresso at this nearby place, then walk through the forum. It filters all its knowledge. about Rome through the lens of a tour guide whose job is to create an efficient, enjoyable experience within specific constraints, time, interests.
00:18:08 Speaker 1
It shifts from just knowing facts to applying those facts practically.
00:18:12 Speaker 2
Totally. Or another example, act as a budget travel planner. That phrase alone forces the AI to consider things a generic knowledge base would ignore.
00:18:22 Speaker 1
Like what.
00:18:23 Speaker 2
Logistics, travel times between sites, opening hours, affordability, maybe even booking strategies or transport options. A travel planner thinks about the doing, not just the knowing.
00:18:32 Speaker 1
So the role forces a specific kind of thinking process.
00:18:35 Speaker 2
Yes. The quality of the output when you assign a role, it just jumps to another level. It dramatically narrows the AI's focus and makes it apply specialized filters and priorities to the information it accesses.
00:18:46 Speaker 1
Okay, I'm sold. Specificity, context, role. Those are the three pillars. That covers what? 90% of good prompting.
00:18:52 Speaker 2
Easily 90%, probably more for most everyday tasks. If you just nail those three. You're already way ahead of the curve. But yeah, there are a few bonus tricks, little techniques that can take prompts from just good or great to consistently brilliant, and they can save you even more time.
00:19:09 Speaker 1
Okay, bonus round. What's the first trick.
00:19:12 Speaker 2
Remember that these tools are conversational AI. The key word is conversation. Meaning? Meaning you don't have to get everything perfect in the very first prompt. It's not a one-shot deal. Often, the absolute best results come from a little bit of back and forth, refining the output.
00:19:27 Speaker 1
So don't be afraid to talk back to the AI.
00:19:29 Speaker 2
Exactly. Challenge it. Ask it to clarify. Tell it what you liked or didn't like about its first attempt. People often get a long, detailed answer and think, okay, job done. But the real power comes from iterating on that first draft.
00:19:42 Speaker 1
Okay, let's walk through an example of that refinement cycle. Say I upload, I don't know, a really dense 20-page legal document about new cryptocurrency regulations.
00:19:52 Speaker 2
Okay, complex input.
00:19:53 Speaker 1
My first prompt, using our rules, might be, Compliance.
00:20:08 Speaker 2
Good prompt. Specific, context, role. What does the AI give back.
00:20:14 Speaker 1
Let's say it gives back a list of 10 risks, but they're super dense, full of legal jargon, hard to understand for anyone who isn't a lawyer.
00:20:21 Speaker 2
Okay, typical first output for that kind of task. So what's your next move? Not starting over, right.
00:20:25 Speaker 1
Right. My next move is a conversational follow-up. Something simple like, okay, thanks. Now act as a high school civics teacher. Change the role.
00:20:32 Speaker 2
Nice pivot.
00:20:32 Speaker 1
Rephrase those exact same 10 risks using simple, plain English. No legal jargon at all. This needs to be suitable for presenting to our non-technical marketing team.
00:20:42 Speaker 2
Brilliant. What happens next.
00:20:43 Speaker 1
The AI spits out the simplified list. Much clearer. But maybe I need it even more actionable. So one more follow-up. Perfect. Now put those 10 simplified risks into a simple two-column markdown table. In the first column, list the risk. In the second column, suggest just one single column. concrete, actionable mitigation step we can take for each risk.
00:21:04 Speaker 2
See what you did there. Three quick, simple conversational prompts.
00:21:07 Speaker 1
Yeah.
00:21:07 Speaker 2
You went from a dense, unusable legal document to a clear, actionable risk presentation for a specific audience, complete with suggested actions, all formatted neatly. How long would that take a human to do manually.
00:21:21 Speaker 1
Hours. At least. Reading the doc, summarizing, simplifying, brainstorming actions, formatting. Yeah. Hours.
00:21:28 Speaker 2
Exactly. That cycle prompt, get output, critique, refine, reformat. That's where the massive time savings really come in. You're using the AI's power to refilter and restructure its own previous output based on your evolving needs.
00:21:41 Speaker 1
Okay. So embrace the conversation. Bonus trick, hashtag one. What's hashtag two.
00:21:45 Speaker 2
Trick hashtag two is about controlling the format of the output. This ties into the refinement we just talked about, but it's worth focusing on specifically.
00:21:52 Speaker 1
Presentation matters, not just content.
00:21:54 Speaker 2
Hugely. Because often the value isn't just in the information. It's how quickly and easily you, You can process that information and put it to use.
00:22:02 Speaker 1
If you get back a giant wall of text.
00:22:05 Speaker 2
Exactly. If you're doing complex research, like comparing several different options or analyzing competitors, getting the results back as one long paragraph is really inefficient. Your brain still has to do all the hard work of pulling out the key comparisons and organizing the structure.
00:22:21 Speaker 1
So we need to explicitly tell the AI how to structure the information visually.
00:22:25 Speaker 2
Yes. Use structural language in your prompt. For example, let's say you're comparing three different database technologies, maybe traditional SQL, NoSQL, and a graph database. Okay. Don't just ask, compare these three databases. Be specific about the format. Prompt something like, act as a senior database architect. Compare the pros and cons of SQL, NoSQL, and graph databases for a social media application. Specificity, role, context. Structure your comparison as a single markdown table.
00:22:53 Speaker 1
I get demanding a table.
00:22:54 Speaker 2
The table should have columns for. Primary use case strengths, key scalability challenges. and typical learning curve for developers. Make sure the information in each cell is presented as concise bullet points.
00:23:08 Speaker 1
Very precise formatting instructions.
00:23:11 Speaker 2
Asking for that markdown table or a bulleted list, or maybe even JSON format if you're feeding it into another program. It's not just about making it look pretty.
00:23:19 Speaker 1
It's a cognitive shortcut.
00:23:21 Speaker 2
It's a massive cognitive shortcut. It means you can instantly grasp the comparisons. You can copy-paste that perfectly formatted table directly into a report, a presentation slide, a wiki page, whatever. You save all that time you would have spent manually reformatting and restructuring the AI's output.
00:23:38 Speaker 1
Okay, control the format for easier processing. Makes sense. What's the third bonus trick? You mentioned it earlier and said it was a personal favorite.
00:23:45 Speaker 2
Ah, yes, the jargon filter. Or, more broadly, setting explicit limits, especially simplification limits.
00:23:50 Speaker 1
This is the explain it like I'm 12 trick.
00:23:52 Speaker 2
That's the ultimate version of it, yeah. And it's phenomenally useful, especially when you're trying to get up to speed on a complex topic.
00:24:01 Speaker 1
Because left to its own devices.
00:24:03 Speaker 2
Left to its own devices, especially on technical or academic subjects, the AI will often default to the most technically precise, academically rigorous explanation it can generate, which is usually filled with jargon you don't understand.
00:24:17 Speaker 1
Right. If I ask it about, I don't know, quantum entanglement.
00:24:20 Speaker 2
You'll get something that sounds like it's straight out of a physics textbook.
00:24:23 Speaker 1
Yeah.
00:24:23 Speaker 2
Completely accurate, probably, but totally inaccessible if you're not a physicist. It assumes you want the full, complex picture.
00:24:31 Speaker 1
So you have to force it to simplify.
00:24:33 Speaker 2
You have to force it. You set an explicit constraint on the audience, and therefore the vocabulary. And the most extreme, but often most effective version is exactly that. Explain complex topic to me as if I were 12 years old.
00:24:45 Speaker 1
What does that command actually do inside the AI.
00:24:48 Speaker 2
It triggers what you might call forced simplification. It compels the AI to strip out basically all the specialized terminology, all the academic nuances. And hedging all the assumed prior.
00:25:00 Speaker 1
And just give you the core idea.
00:25:02 Speaker 2
Exactly. It forces it down to the fundamental concept, often using analogies or simpler language. It's fantastic for quickly grabbing the essence of anything from, I don't know, blockchain technology to Keynesian economics to how CRISPR works.
00:25:17 Speaker 1
That's super useful for learning. Are there other ways to use limit setting professionally.
00:25:21 Speaker 2
Definitely. Another key area is managing length and focus, especially when dealing with really long documents or when you need a concise summary. Remember, LLMs have limits on how much text they can process at once, the context window or token limit.
00:25:35 Speaker 1
Right. You can't just feed it a 500-page book and expect a perfect summary instantly.
00:25:39 Speaker 2
Exactly. So if you're working with a massive research report, say 10,000 words, you need to constrain the output. A great limit setting prompt would be analyze this entire 10,000-word market report I've uploaded. Summarize the key findings relevant to regulatory obstacles for market expansion into the European Union only. The summary must be exactly the same as the key findings. 350 words long.
00:26:02 Speaker 1
Okay, so you're constraining both the topic focus, e-regulatory obstacles only, and the length, 350 words.
00:26:08 Speaker 2
Precisely. That ensures the AI uses its limited processing power efficiently. It doesn't waste tokens summarizing the historical background or methodology sections. It focuses only on the high-value, specific information you need delivered in the concise format you requested.
00:26:23 Speaker 1
So constraint isn't just about simplifying language. It's also about forcing focus and managing the AI's limitations. Yeah, it's a really powerful angle.
00:26:32 Speaker 2
It absolutely is. That deliberate use of constraint, whether it's simplifying language, setting length limits, or focusing the topic, it's a secret weapon for getting efficient, targeted knowledge acquisition.
00:26:42 Speaker 1
Fantastic. Okay, let's bring this deep dive home. We've covered a lot of ground here. It feels like the core message is about shifting our mindset, doesn't it.
00:26:49 Speaker 2
Absolutely. It's about moving away from being just a passive recipient of whatever the AI spits out.
00:26:56 Speaker 1
And becoming the active, intentional director of this incredibly powerful, special...
00:27:00 Speaker 2
intelligence. You got it. You're the conductor of the orchestra. So let's recap that final.
00:27:04 Speaker 1
checklist for everyone listening. The three pillars, the absolute must-dos for writing a high-quality prompt. Okay. Number one, be specific. Nail down the what. No vague language. Define the scope, the time frame, the desired output, the format, all those constraints. Check. Number two, give context. Provide the why, the background, the audience, the purpose. This gives the AI the real-world utility, the tone, the strategic objective. Check. And number three, tell it the role you need it to play. Assign it a job. This applies that crucial specialization filter,
00:27:38 Speaker 1
unlocking deeper customized expertise. The big three. Yeah. Specificity, context, role. Master those three. Maybe sprinkle in those bonus tricks we talked about. Embracing the conversation.
00:27:50 Speaker 2
controlling the format, setting limits. And you've basically mastered the tool. Seriously. You've unlocked access to the tool. You've unlocked this incredible team of on-demand specialists. Market researchers, lawyers, programmers, travel agents, historians, CTOs, whatever you need.
00:28:06 Speaker 1
Available instantly.
00:28:08 Speaker 2
And this really isn't just about getting slightly nicer documents or summaries. This is about fundamentally changing workflows. It's about drastically compressing the time it takes you to learn complex subjects, to analyze data, to plan projects, to create really sophisticated, high-value work.
00:28:24 Speaker 1
It feels like the quality of our output, professionally or personally, is now becoming much more directly linked to the quality of our prompts, quality of our questions.
00:28:33 Speaker 2
It absolutely is. The bottleneck is shifting from execution time to instruction quality.
00:28:38 Speaker 1
So for everyone listening, you now have the keys. You understand how to ask much better, much more intelligent, much more targeted questions to these powerful AI systems. So here's the final thought to leave you with. Now that you possess this ability, this key to unlock. Unlocking a truly powerful, specialized assistant. What profound peace of mind. are you finally going to uncover? Or maybe what ambitious project, something you've been putting off, are you finally going to kickstart now that you have this incredibly focused assistant ready.
00:29:08 Speaker 1
and waiting for your command?