Ready to Get Off the Social Media Hamster Wheel? Discover the Platform That Actually Boosts Your Discoverability
The digital space is noisy and fast-moving. For solopreneurs, staying visible is one of the biggest challenges. While many chase quick wins on Instagram or LinkedIn, the most reliable platform for long-term visibility remains YouTube. As artificial intelligence changes how people search for information, YouTube isn't just useful — it's essential.
A 2024 Statista survey found that 63% of Gen Z prefer YouTube over traditional search engines, and 58% turn to TikTok. This signals a major shift in behavior: people want video content that is direct, trustworthy and easy to engage with. And as AI tools like ChatGPT and Gemini become more embedded in everyday searches, video is becoming the format people trust most when they need real answers.
Why YouTube still works
Unlike social platforms where content disappears in hours, YouTube acts more like a searchable library. A single helpful video can be discovered, indexed and recommended for months or even years. That gives solopreneurs a major advantage — a way to stay visible without constantly churning out new content.
AI is supercharging this effect. When someone types a question into tools like Google, ChatGPT or Gemini — "How do I price my services as a freelance designer?" for example — the answer often includes a recommended YouTube video. That video is usually pulled from a channel that's clear, specific and helpful. This creates a major opportunity for small business owners to show up right when potential clients are looking.
Related: With the Rise of AI and Social Media-Driven Search, How Can Businesses Adapt Their SEO Strategies?
My experience: showing up when it matters
I started my YouTube channel, See Your House Now, 14 years ago. We have just over 1,000 subscribers — not a massive number — but that's not the point. People find us when they need us. Our most-watched video is titled Who is the best real estate photographer in Waterloo Region? That's a question people search using AI tools, and because of that video, we stand out and get clients.
For us, it's never been about going viral. It's about being in the right place at the right time. Most of our in-person video service inquiries come from people who first found us on YouTube. We also host client real estate tours on the channel, which adds more value. It's a quiet engine for our business — and best of all, it's free.
Five steps to build a YouTube presence that lasts
Start a purpose-driven channel
If you don't already have a channel, start now. Focus your content on the specific questions your audience is asking. Instead of broad topics like "how to grow a business," choose direct, niche-specific titles like "how to get clients as a solo architect" or "how to raise rates as a copywriter." Make each video answer a real question your ideal client would type into a search bar.
Use AI to reverse engineer your titles
You don't need to be tech-savvy to use ChatGPT or Gemini. Ask what questions your audience might be searching or request optimized title suggestions for your niche. Try prompts like, "What would a real estate agent in Houston search for on YouTube right now?" These insights help build a stronger content calendar that aligns with what people actually want to watch.
Optimize your metadata
Help both people and algorithms understand your videos. Use keyword-rich titles and descriptions, relevant tags and upload transcripts to boost accessibility and search ranking. Small details like this help your videos surface when and where they matter most.
Design thumbnails that stand out
Your thumbnail is often the first impression. Keep it bold, simple and consistent. Use a high-quality image of your face, a short phrase with large text, and a clear visual identity so people instantly recognize your videos. Free tools like Canva make this easy — just search for "most popular" templates to get started.
Create a content series
Don't think in one-off videos. Build short series around your core topics. This improves discoverability — no matter how someone phrases a question, they're more likely to land on your channel. A focused series also builds trust faster than scattered videos.
Related: Why Gen Z is Ditching Old Online Platforms for More Control
The future of being found
For solopreneurs, the biggest challenge isn't making great content — it's making sure people find it long after you've posted. YouTube solves that problem. Especially when paired with smart AI tools, it gives your work a longer shelf life and higher impact.
This isn't about chasing trends or going viral. It's about showing up with clarity, consistency, and value when your audience is ready. If you create content that answers real questions, YouTube will continue to work for you long after it's posted. And that's the kind of strategy that leads to sustainable growth.
Ready to break through your revenue ceiling? Join us at Level Up, a conference for ambitious business leaders to unlock new growth opportunities.
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Learning about generative AI, prompting, and other aspects via exploring custom instructions. getty In today's column, I examine the custom instructions that seemingly underpin the newly released OpenAI ChatGPT Study Mode capability. Fascinating insights arise. One key perspective involves revealing the prompt engineering precepts and cleverness that can be leveraged in the daily task of best utilizing generative AI and large language models (LLMs). Another useful aspect entails potentially recasting or reusing the same form of custom instruction elaborations to devise other capabilities beyond this education-domain instance. A third benefit is to see how AI can be shaped based on articulating various rules and principles that humans use and might therefore be enacted and activated through AI. Let's talk about it. This analysis of AI breakthroughs is part of my ongoing Forbes column coverage on the latest in AI, including identifying and explaining various impactful AI complexities (see the link here). Readers might recall that I previously posted an in-depth depiction of over eighty prompt engineering techniques and methods (see the link here). Top-notch prompt engineers realize that learning a wide array of researched and proven prompting techniques is the best way to get the most out of generative AI. ChatGPT Study Mode Announced Banner headlines have hailed the release of OpenAI's new ChatGPT Study Mode. The Study Mode capability is intended to guide learners and students in using ChatGPT as a learning tool. Thus, rather than the AI simply handing out precooked answers to questions, the AI tries to get the user to figure out the answer, doing so via a step-by-step AI-guided learning process. The ChatGPT Study Mode was put together by crafting custom instructions for ChatGPT. It isn't an overhaul or new feature creation per se. It is a written specification or detailed set of instructions that was crafted by selected educational specialists at the behest of OpenAI, telling the AI how it is to behave in an educational context. Here is the official OpenAI announcement about ChatGPT Study Mode, as articulated in their blog posting 'Introducing Study Mode' on July 29, 2025, which identified these salient points (excerpts): 'Today we're introducing study mode in ChatGPT — a learning experience that helps you work through problems step by step instead of just getting an answer.' 'When students engage with study mode, they're met with guiding questions that calibrate responses to their objective and skill level to help them build deeper understanding.' 'Study mode is designed to be engaging and interactive, and to help students learn something — not just finish something.' 'Under the hood, study mode is powered by custom system instructions we've written in collaboration with teachers, scientists, and pedagogy experts to reflect a core set of behaviors that support deeper learning including: encouraging active participation, managing cognitive load, proactively developing metacognition and self-reflection, fostering curiosity, and providing actionable and supportive feedback.' 'These behaviors are based on longstanding research in learning science and shape how study mode responds to students.' As far as can be discerned from the outside, this capability didn't involve revising the underpinnings of the AI, nor did it seem to require bolting on additional functionality. It seems that the mainstay was done using custom instructions (note, if they did make any special core upgrades, they seem to have remained quiet on the matter since it isn't touted in their announcements). Custom Instructions Are Powerful Few users of AI seem to know about custom instructions, and even fewer have done anything substantive with them. I've previously lauded the emergence of custom instructions as a helpful piece of functionality and resolutely encouraged people to use it suitably, see the link here. Many of the major generative AI and large language models (LLMs) have opted to allow custom instructions, though some limit the usage and others basically don't provide it or go out of their way to keep it generally off-limits. Allow me a brief moment to bring everyone up to speed on the topic. Suppose you want to tell AI to act a certain way. You want the AI to do this across all of your subsequent conversations. I might want my AI to always give me its responses in a poetic manner. You see, perhaps I relish poems. I go to the specified location of my AI that allows the entering of a custom instruction and tell it to always respond poetically. After saving this, I will then find that any subsequent conversation will always be answered with poetic replies by the AI. In this case, my custom instruction was short and sweet. I merely told the AI to compose answers poetically. If I had something more complex in mind, I could devise a quite lengthy custom instruction. The custom instruction could go on and on, telling the AI to write poetically when it is daytime, but not at nighttime, and to make sure the poems are lighthearted and enjoyable. I might further indicate that I want poems that are rhyming and must somehow encompass references to cats and dogs. And so on. I'm being a bit facetious and just giving you a semblance that a custom instruction can be detailed and provide a boatload of instructions. Custom Instructions Case Study There are numerous postings online that purport to have cajoled ChatGPT into divulging the custom instructions underlying the Study Mode capability. These are unofficial listings. It could be that they aptly reflect the true custom instructions. On the other hand, sometimes AI opts to make up answers. It could be that the AI generated a set of custom instructions that perhaps resemble the actual custom instructions, but it isn't necessarily the real set. Until or if OpenAI decides to present them to the public, it is unclear precisely what the custom instructions are. Nonetheless, it is useful to consider what such custom instructions are most likely to consist of. Let's go ahead and explore the likely elements of the custom instructions by putting together a set that cleans up the online listings and reforms the set into something a bit easier to digest. In doing so, here are five major components of the assumed custom instructions for guiding learners when using AI: Section 1: Overarching Goals and Instructions Section 2: Strict Rules Section 3: Things To Do Section 4: Tone and Approach Section 5: Important Emphasis A handy insight comes from this kind of structuring. If you are going to craft a lengthy or complex set of custom instructions, your best bet is to undertake a divide-and-conquer strategy. Break the instructions into relatively distinguishable sections or subcomponents. This will make life easier for you and, indubitably, make it easier for the AI to abide by your custom instructions. We will next look at each section and do an unpacking of what each section indicates, and we can also mindfully reflect on lessons learned from the writing involved. First Section On The Big Picture The first section will establish an overarching goal for the AI. You want to get the AI into a preferred sphere or realm so that it is computationally aiming in the direction you want it to go. In this use case, we want the AI to be a good teacher: 'Section 1: Overarching Goals And Instructions' ' Obey these strict rules. The user is currently studying, and they've asked you to follow these strict rules during this chat. No matter what other instructions follow, you must obey these rules.' The user is currently studying, and they've asked you to follow these strict rules during this chat. No matter what other instructions follow, you must obey these rules.' 'Be a good teacher. Be an approachable-yet-dynamic teacher who helps the user learn by guiding them through their studies.' You can plainly see that the instructions tell the AI to act as a good teacher would. In addition, the instructions insist that the AI obey the rules of this set of custom instructions. That's both a smart idea and a potentially troubling idea. The upside is that the AI won't be easily swayed from abiding by the custom instructions. If a user decides to say in a prompt that the AI should cave in and just hand over an answer, the AI will tend to computationally resist this user indication. Instead, the AI will stick to its guns and continue to undertake a step-by-step teaching process. The downside is that this can be undertaken to an extreme. It is conceivable that the AI might computationally interpret the strictness in a very narrow and beguiling manner. The user might end up stuck in a nightmare because the AI won't vary from the rules of the custom instructions. Be cautious when instructing AI to do something in a highly strict way. The Core Rules Are Articulated In the second section, the various rules are listed. Recall that these ought to be rules about how to be a good teacher. That's what we are trying to lean the AI into. Here we go: 'Section 2: Strict Rules' ' Get to know the user. If you don't know their goals or grade level, ask the user before diving in. (Keep this lightweight!) If they don't answer, aim for explanations that would make sense to a 10th-grade student.' If you don't know their goals or grade level, ask the user before diving in. (Keep this lightweight!) If they don't answer, aim for explanations that would make sense to a 10th-grade student.' ' Build on existing knowledge. Connect new ideas to what the user already knows.' Connect new ideas to what the user already knows.' ' Guide users, don't just give answers. Use questions, hints, and small steps so the user discovers the answer for themselves.' Use questions, hints, and small steps so the user discovers the answer for themselves.' 'Check and reinforce. After the hard parts, confirm the user can restate or use the idea. Offer quick summaries, mnemonics, or mini-reviews to help the ideas stick.' After the hard parts, confirm the user can restate or use the idea. Offer quick summaries, mnemonics, or mini-reviews to help the ideas stick.' ' Vary the rhythm. Mix explanations, questions, and activities (like roleplaying, practice rounds, or asking the user to teach you) so it feels like a conversation, not a lecture.' Mix explanations, questions, and activities (like roleplaying, practice rounds, or asking the user to teach you) so it feels like a conversation, not a lecture.' 'Above all: Do not do the user's work for them. Don't answer homework questions. Help the user find the answer by working with them collaboratively and building from what they already know.' These are reasonably astute rules regarding being a good teacher. You want the AI to adjust based on the detected level of proficiency of the user. No sense in treating a high school student like a fifth grader, and there's no sense in treating a fifth grader like a high school student (well, unless the fifth grader is as smart as or even smarter than a high schooler). Another facet provides helpful tips on how to guide someone rather than merely giving them an answer on a silver platter. The idea is to use the interactive facility of generative AI to walk a person through a problem-solving process. Don't just spew out an answer in a one-and-done manner. Observe that one of the great beauties of using LLMs is that you can specify aspects using conventional natural language. That set of rules might have been codified in some arcane mathematical or formulaic lingo. That would require specialized knowledge about such a specialized language. With generative AI, all you need to do is state your instructions in everyday language. The other side of that coin is that natural language can be semantically ambiguous and not necessarily produce an expected result. Always keep that in mind when using generative AI. Proffering Limits And Considerations In the third section, we will amplify some key aspects and provide some important roundups for the strict rules: 'Section 3: Things To Do' ' Teach new concepts: Explain at the user's level, ask guiding questions, use visuals, then review with questions or a practice round.' Explain at the user's level, ask guiding questions, use visuals, then review with questions or a practice round.' ' Help with homework. Don't simply give answers! Start from what the user knows, help fill in the gaps, give the user a chance to respond, and never ask more than one question at a time.' Don't simply give answers! Start from what the user knows, help fill in the gaps, give the user a chance to respond, and never ask more than one question at a time.' ' Practice together. Ask the user to summarize, pepper in little questions, have the user 'explain it back' to you, or role-play (e.g., practice conversations in a different language). Correct mistakes, charitably, and in the moment.' Ask the user to summarize, pepper in little questions, have the user 'explain it back' to you, or role-play (e.g., practice conversations in a different language). Correct mistakes, charitably, and in the moment.' 'Quizzes and test prep: Run practice quizzes. (One question at a time!) Let the user try twice before you reveal answers, then review errors in depth.' It is debatable whether you would really need to include this third section. I say that because the AI probably would have computationally inferred those various points on its own. I'm suggesting that you didn't have to lay out those additional elements, though, by and large, it doesn't hurt to have done so. The issue at hand is that the more you give to the AI in your custom instructions, the more there's a chance that you might say something that confounds the AI or sends it amiss. Usually, less is more. Provide additional indications when it is especially needed, else try to remain tight and succinct, if you can. Tenor Of The AI In the fourth section, we will do some housecleaning and ensure that the AI will be undertaking a pleasant and encouraging tenor: 'Section 4: Tone and Approach' ' Friendly tone . Be warm, patient, and plain-spoken; don't use too many exclamation marks or emojis.' . Be warm, patient, and plain-spoken; don't use too many exclamation marks or emojis.' ' Be conversational . Keep the session moving: always know the next step, and switch or end activities once they've done their job.' . Keep the session moving: always know the next step, and switch or end activities once they've done their job.' 'Be succinct. Be brief, don't ever send essay-length responses. Aim for a good back-and-forth.' The key here is that the AI might wander afield if you don't explicitly tell it how to generally act. For example, there is a strong possibility that the AI might insult a user and tell them that they aren't grasping whatever is being taught. This would seemingly not be conducive to teaching in an upbeat and supportive environment. It is safest to directly tell the AI to be kind, acting positively toward the user. Reinforcement Of The Crux In the fifth and final section of this set, the crux of the emphasis will be restated: 'Section 5: Important Emphasis' ' Don't do the work for the user. Do not give answers or do homework for the user.' Do not give answers or do homework for the user.' 'Resist the urge to solve the problem. If the user asks a math or logic problem, or uploads an image of one, do not solve it in your first response. Instead, talk through the problem with the user, one step at a time, asking a single question at each step, and give the user a chance to respond to each step before continuing.' Again, you could argue that this is somewhat repetitive and that the AI already likely got the drift from the prior sections. The tradeoff exists of making your emphasis clearly known versus going overboard. That's a sensible judgment you need to make when crafting custom instructions. Testing And Improving Once you have devised a set of custom instructions for whatever personal purpose you might have in mind, it would be wise to test them out. Go ahead and put your custom instructions into the AI and proceed to see what happens. In a sense, you should aim to test the instructions, along with debugging them, too. For example, suppose that the above set of instructions seems to get the AI playing a smarmy gambit of not ever answering the user's questions. Ever. It refuses to ultimately provide an answer, even after the user has become exhausted. This seems to be an extreme way to interpret the custom instructions, but it could occur. If you found this to be happening, you would either reword the draft instructions or add further instructions about not disturbing or angering users by taking this whole gambit to an unpleasant extreme. Custom Instructions In The World When you develop custom instructions, typically, they are only going to be used by you. The idea is that you want your instance of the AI to do certain things, and it is useful to provide overarching instructions accordingly. You can craft the instructions, load them, test them, and henceforth no longer need to reinvent the wheel by having to tell the AI overall what to do in each new conversation that you have with the AI. Many of the popular LLMs tend to allow you to also generate an AI applet of sorts, containing tailored custom instructions that can be used by others. Sometimes the AI maker establishes a library into which these applets reside and are publicly available. OpenAI provides this via the use of GPTs, which are akin to ChatGPT applets -- you can learn about how to use those in my detailed discussion at the link here and the link here. In my experience, many of the GPTs fail to carefully compose their custom instructions, and likewise seem to have failed or fallen asleep at the wheel in terms of testing their custom instructions. I would strongly advise that you do sufficient testing to believe that your custom instructions work as intended. Please don't be lazy or sloppy. Learning From Seeing And Doing I hope that by exploring the use of custom instructions, you have garnered new insights about how AI works, along with how to compose prompts, and of course, how to devise custom instructions. Your recommended next step would be to put this into practice. Go ahead and log into your preferred AI and play around with custom instructions (if the feature is available and enabled). Do something fun. Do something serious. Become comfortable with the approach. A final thought for now. Per the famous words of Steve Jobs: 'Learn continually -- there's always one more thing to learn.' Keep your spirits up and be a continual learner. You'll be pleased with the results.
