Q-Answers

qanswers

Maho Bay, St. Thomas, USVI How I learned to code The first week or so of my winter break as a high school freshman, I went to the wonderful, isolated, internet-less Maho Bay in St. John Island with my family. Inspired by the unconnectedness or perhaps in a reaction against it, I was bursting with ideas for social networks and mobile apps and website designs and everything else Internet throughout the entire trip. When I returned home and was re-united with my MacBook, I immediately sat down, fired up Vim, and started trying to build something. Mobile Me’s login page, circa 2010. Instead of “Member Name” and “Password” appearing outside of the respective input boxes, the labels are positioned on top of them. I can’t remember what the product idea was, exactly, that I was so excited about — but I do remember that, within the first two days of coding, I ran into a problem: I was a big fan of the placeholder text, later standardized by HTML5, that Apple had pioneered in their login page for MobileMe, and I wanted to have similar functionality in app’s login page. But I had no idea how to approach the problem. I didn’t even know the terminology for what I was trying to create, so my attempts to search for answers had been futile. my For a professional programmer working on a professional project, a problem like this would be no big deal. The feature would be marked with a high implementation cost in the internal issue tracker, the programmer would probably move on to more pressing issues, and maybe eventually circle back with the help of someone on the team who had experience with creating custom elements. <input /> And for a student taking a formal web programming course, this problem wouldn’t be too bad, either. It’s tough, so the assignment would probably have included a guided approach. And there’s always office hours if help is needed from, as with the professional, someone who already knows most of the answer. But I was not a professional programmer or a formal computer science student: I was a fourteen-year-old, self-taught, inexperienced coder working on an independent passion project. I didn’t (yet) have the feature prioritization skills to put the placeholders on hold while I worked on other, higher-bang-for-buck functionality. I didn’t have anyone working on the problem with me. I didn’t know anyone who had done much of with elements, let alone customize them. And so this was the kind of frustrating issue that had the potential to derail my project with large amounts of unnecessary wheel-spinning, and to ultimately make me less likely to keep programming at all. anything <input /> Problems like these—problems where you have to develop a problem-solving approach in totally unfamiliar digital terrain while simultaneously learning to work around the environment’s bugs, edge-cases and weirdness, and are also lacking the vocabulary to even articulate what you’re doing—are why coding is hard. They’re also why most people tackle both learning to code and building things with code in highly institutional environments where a lot of structural support exists. Without someone experienced as a guide, figuring out these kinds of problems is close to impossible. A typical phpBB forum (top) and 2010-era StackOverflow (bottom). Because I was too young for formal classes and definitely far too young for employment when I started learning to code, I resorted to the next-best-thing: repeatedly Googling my problems until possibly coming to a solution. In the beginning, the results for most programming queries were dominated by a couple of old, phpBB-powered of programming communities and the more commercial Experts-Exchange. They worked, more or less: after going through many pages of questions and results (each of which had to be read and tried out to see if it worked), a solution would generally present itself. Of course, sometimes it didn’t. In those cases I often just gave up. “Obvious” questions asked by “n00bs” were ridiculed along with the questioner herself — and God forbid you answered a question incorrectly. The putdowns were funny, but they also made me far too self-conscious and shy to ever try participating myself, lest be the subject of ridicule. I In 2009, I began tackling more and more ambitious projects and, just in time, Google results began taking me, more and more, to a new forum: StackOverflow. In retrospect, it was the incredible attention that Jeff and Joel dedicated to creating an answer-driven, focused, developer-friendly user experience that made the site great — but all I remember from the time is telling people how cool it was that StackOverflow would almost always have an immediate, working solution to my problem. In addition to the immediate readiness of answers compared to its forum predecessors, the other big difference between StackOverflow and everything that existed before it was how the community was compared to older forums. Partly because of strict moderation which kept everything focused on civilly and professionally answering questions and partly because of standard UI/UX idioms for dealing with duplicated questions and ranking answers that were helpful for readers and contributors alike, none of the meanness was present on StackOverflow. Even as a fourteen year old, I felt comfortable participating. friendly Which brings us back to my much-desired placeholders from my winter break project. Stumped, I decided to make a last-ditch effort, summoned up some courage, shakily opened up StackOverflow, and asked my first question: input Could anybody tell me how to create something like the hint/labels on Apple’s MobileMe login page (www.me.com) in that the label/hint text just fades when you click on the box and doesn’t go away until the user starts typing? (And if you could use JQuery to do it, it’d be even better.) Thank you very much! (By label/hint text I mean the gray ‘Username’ and ‘Password’ text.) — http://stackoverflow.com/q/2068369/251162 As you can tell from my phrasing in the question, I was pretty unsure of myself. I had, after all, never asked publicly asked a question on the internet before. But, nine minutes after posting, I got an answer with the then-correct terminology and a link to a jQuery plugin doing exactly what I wanted. StackOverflow had, almost instantly, transformed a coding roadblock so frustrating that I almost gave up into an easily surmountable challenge. It was magic. I asked when, a month later, I came across some bizarre syntax while trying to learn Ruby by reading through the Rails source code. Even though I hadn’t quite understood the context of the code, again within minutes, I got a couple of easy-to-understand answers. Just like before, a major coding roadblock had been transformed into an interesting, stimulating challenge. another question This pattern kept repeating. As I tried to understand how the Ruby ecosystem worked Rails, StackOverflow . I was when I asked about the magic of Rails’ hot-updates. I had professional programmers . Twenty questions about Ruby later, when I started learning about C, StackOverflow . It helped me and . It helped me to . And over the course of StackOverflow and its users continued to help me with everything from to . without again came to the rescue pointed in the right direction give me confirmation that a not-quite-right-feeling SQL architecture I had come up with was the right thing to do helped me again to understand the notoriously confusing interaction between JavaScript’s asynchronous callbacks and synchronous state management how the PHP program lifecycle works understand how Python imports work 236 questions, the Scala type system mimicking iOS 7’s blurred backgrounds using <canvas /> Again and again, StackOverflow answered my questions — and, in the process, helped me to keep pushing forward in my journey to learn programming and to build things with code. As I ran into problems and asked questions, I both gained the knowledge to keep progressing in my coding and the practical solutions needed to complete my apps. Looking back, it was just as important in my coding journey that I realized that post to StackOverflow and get help solving almost any programming challenge: it made all of the tough, frustrating parts of learning to code infinitely more manageable. Who knows if I would have kept trying to code without it? could I suspect that communities like StackOverflow, especially when used in conjunction with traditional textbooks, could help resolve the as we try as a society to adjust to requiring . That is, of course, part of the vision of the broader StackExchange network. ongoing problems with using the attempt to directly translate university courses to the more accessible internet more dynamic labor markets constant continuing education outside of classrooms But that’s a topic for another time. For now, I’d just like to say thank you, again, to StackOverflow and everyone who contributes to it. It’s hard to overstate how important being able to code has been for me. Coding has consistently made it easier and more rewarding to study economics at Columbia by enabling automation, analysis, and data-gathering. Coding has made it possible for me to produce projects by myself. Coding was instrumental in getting a summer internship at an MBA-dominated venture capital firm as a college sophomore, and it was even more instrumental in allowing me to succeed there. And coding has made it possible for me to be involved with a number of tackling challenging problems with some truly inspiring people and amazing technologies. I can’t wait to see where code will take me next. quick but impactful really cool projects So once again, thank you, StackOverflow. Thank you for helping me learn how to code, thank you for making my life better.

Thank you, StackOverflow

#AI-chatbot Writing Contest Sponsor, Coze, Shares Secrets for Success as Contest Deadline Approaches

Coze, спонсар пісьменніцкага конкурсу #AI-chatbot, дзеліцца сакрэтамі поспеху па меры набліжэння дэдлайну конкурсу

#AI-chatbot Sponzor takmičenja u pisanju, Coze, dijeli tajne uspjeha kako se približava rok za takmičenje

El patrocinador del concurso de redacción de chatbots con inteligencia artificial, Coze, comparte sus secretos para el éxito a medida que se acerca la fecha límite del concurso

ربات #AI-chat Writing حامی مسابقه، Coze، اسرار موفقیت را با نزدیک شدن به آخرین مهلت مسابقه به اشتراک می گذارد

#AI-chatbot patwone konkou ekriti, Coze, pataje sekrè pou siksè pandan dat limit konkou a ap apwoche

#AI-chatbot íróverseny szponzora, Coze, megosztja a siker titkait a verseny határidejének közeledtével

#AI チャットボット ライティング コンテストのスポンサー Coze が、コンテストの締め切りが近づく中、成功の秘訣を披露

#AI-챗봇 글쓰기 대회 스폰서, 코즈, 대회 마감일이 다가오면서 성공 비결 공유

#AI-chatbot Sponsor al concursului de scriere, Coze, împărtășește secretele succesului pe măsură ce se apropie termenul limită al concursului

Спонсор письменницького конкурсу #AI-chatbot Coze ділиться секретами успіху з наближенням дедлайну конкурсу

Nhà tài trợ cuộc thi viết #AI-chatbot, Coze, chia sẻ bí quyết thành công khi thời hạn cuộc thi đang đến gần

#AI 聊天机器人写作比赛赞助商 Coze 在比赛截止日期临近之际分享成功秘诀

No, You Don't Need a Highly Specialized Team to Build Your SaaS Application 

What's Next for AI: Interpreting Anthropic CEO's Vision

Què hi ha a continuació per a la IA: interpretar la visió del CEO antròpic

Co bude dál pro AI: Interpretace vize generálního ředitele společnosti Anthropic

Τι ακολουθεί για την AI: Ερμηνεύοντας το όραμα του CEO της Anthropic

¿Qué viene después en el mundo de la IA?: interpretación de la visión del director ejecutivo de Anthropic

AIの今後: Anthropic CEOのビジョンを解釈する

Nini ekoya sima pona AI: Ko interpréter Vision ya CEO Anthropic

AI-ийн дараагийн зүйл: Anthropic CEO-ийн алсын харааг тайлбарлах нь

Seo se Latelago bakeng sa AI: Go Hlatholla Pono ya CEO ya Anthropic

Leswi landzelaka eka AI: Ku hlamusela Xivono xa CEO wa Anthropic

Що далі для ШІ: інтерпретація бачення генерального директора Anthropic

AI uchun navbatdagi narsa: Antropik bosh direktorning qarashlarini talqin qilish

人工智能的下一步：解读 Anthropic 首席执行官的愿景

I was wrong. 18 months ago, I launched @storyarb to be the best content agency for founders & executives. My vision was a $10,000,000/year ghostwriting business. SPOILER: we pivoted. But the future has never looked brighter. Today, storyarb is a B2B company's outsourced Head of Content Marketing. We help our partners create high-quality, niche content for their ICP, while driving scalable demand & pipeline. Keep reading (below) for the full story... I launched storyarb to be the best content agency for founders & executives. I saw the acceleration of short-form video agencies (viralcuts, clipt, shortzy), but felt like there were no businesses supporting execs on text-based social (twitter & linkedin). From my own experience (and that of other founder friends), I knew that Twitter/Linkedin could be a magnet of opportunity. From my own experience (and that of other founder friends), I knew that Twitter/Linkedin could be a magnet of opportunity. It’s how I’ve met amazing entrepreneurs like @RomeenSheth , @jspujji , @alexisohanian.
It’s how I’ve gotten 8-figures worth of advertisers at @MorningBrew.
And it’s how I’ve worked through business ideas, company challenges, & thoughts on where the world is going. At the same time, I knew most founders didn’t have the chops or the time to create great content, solo on social. Which led to a literal shower thought. What if I pair founders with world-class strategists & copywriters. A strategist would pull unique insights from the founder & help set their social strategy. A copywriter would translate ideas into sharp content. Seems logical, right? Personal brand is a nice-to-have, not a need-to-have for founders. The result was 5-6 months of smooth sailing with a client followed by the gutpunch… “Can we catch up?” Churn for exec social became unpalatable. It was driven by: Founders becoming impatient & expecting results sooner.
Founders being pulled in 50 directions & lacking time for social content.
Founders feeling like their internet voice was slightly different from their real life voice. My cofounder, @AbbyMurray, and I knew that we had to get off the hamster wheel if we wanted to build a $10m biz. So we did two things: First, we asked ourselves: “What unfair advantages do we have as a business today?” “The best writers & strategists on the internet, period. We have better ideas & execution.” Second, we listened to our customers (and honestly, no one else). We reflected on past convos: A B2B HR Tech co asked if we could write their newsletter
An e-comm SaaS founder asked for help with e-books
A CMO wanted a company social revival We also interviewed the shit out of our customers. We asked them if they had any other content needs. We asked their marketing teams about their biggest current challenges. We asked “if you had access to world-class writers/content minds, how would you use them?” The answer (and our pivot) became clear. Founders kinda need content help. B2B companies reallyyyy need content help. Marketing teams are scrambling to stand out & grow their business today. Marketing costs are rising (adios cookies, bye zero interest rates, hello increased competition)
Product differentiation is narrowing (software + AI begets more software + AI)
Attribution has become religion, which forces marketers into a box of undifferentiated content
Most brands are living in a sea of sameness (groupthink + AI tooling)
Headcount is recalibrating (we no longer live in pre-COVID La La Land) Which means every marketer & every company is looking to stand out creatively & acquire customers cheaply & scalably. High-quality, high-value content that drives demand is one of the answers. But marketing orgs aren’t equipped to build & execute stand-out content strategies for 5 main reasons: They don’t know how to hire great content talent (copywriters, video creators, producers, strategists), because they’ve never had to do so.
They have marketers doing a lot of the content work, but are starting to realize it’s a completely different skill from most marketing responsibilities.
They have 1-2 team members creating content, but it feels flat & they have no idea what the hell to do now.
They do a poor job of tracking performance & connecting content to pipeline.
Content isn’t centralized...so it’s good in some places, fine in others, and f*cking atrocious elsewhere. Today, storyarb is your outsourced Head of Content Marketing, helping to resuscitate your company’s lifeless content (no matter which situation you’re in). It starts with us understanding the state of your content today. This includes a literal content colonoscopy up & down the org: Newsletters
Email marketing
Company social
Founder social
Ad creative
Website copy
Blog posts
Lead magnets
SOPs/technical docs
[FILL IN THE BLANK] Next, our strategists & writers work in lockstep to UNF*CK YOUR CONTENT by answering one crucial question: “What strategy (read: channels & ideas) will allow us to drive scalable customer demand without sacrificing the quality of our content?” That was the pivot. And it’s working in a big way. Series C tech startups to publicly traded multinationals trust us to be their Head of Content Marketing & we’re driving pipeline with high-quality, niche content built for our customers’ ICP. A few final notes as I reflect on the experience & journey building @storyarb alongside Abby over the last year. Pivots are basically inevitable. The odds that your day 1 product is your day 365 product is close to zero. It’s impossible to make perfect assumptions pre-launch & what’s most important is how you sift through info from your customers & make intuitive & informed decisions thereafter.


There is a business to build around founder social, it’s just not a recurring revenue agency. If I was to build a founder/exec social-only business again, it’d be a combo of courses (organized by platform & expertise), corporate workshops & paid community (accountability/engagement groups).


Building a great business is far more important than being right. As a first-time founder, you want to stay the course & protect your ego. As a second-time founder, you want to make adjustments & build a strong, dependable business.


Great talent creates great competencies. Great competencies create options. The only reason we’ve been able to evolve storyarb & crush it for our current clients is because the business was built on a foundation of exceptional talent. I hope this post made pivoting feel a little less like a failure to you. And if you’re a marketer, I hope this post made you feel like you don’t have to settle for mediocre content, while growing your business. I was wrong. 18 months ago, I launched @storyarb to be the best content agency for founders & executives. My vision was a $10,000,000/year ghostwriting business. @storyarb SPOILER: we pivoted. But the future has never looked brighter. Today, storyarb is a B2B company's outsourced Head of Content Marketing. We help our partners create high-quality, niche content for their ICP, while driving scalable demand & pipeline. Keep reading (below) for the full story... I launched storyarb to be the best content agency for founders & executives. I saw the acceleration of short-form video agencies (viralcuts, clipt, shortzy), but felt like there were no businesses supporting execs on text-based social (twitter & linkedin). From my own experience (and that of other founder friends), I knew that Twitter/Linkedin could be a magnet of opportunity. From my own experience (and that of other founder friends), I knew that Twitter/Linkedin could be a magnet of opportunity. It’s how I’ve met amazing entrepreneurs like @RomeenSheth , @jspujji , @alexisohanian. It’s how I’ve gotten 8-figures worth of advertisers at @MorningBrew. And it’s how I’ve worked through business ideas, company challenges, & thoughts on where the world is going. It’s how I’ve met amazing entrepreneurs like @RomeenSheth , @jspujji , @alexisohanian . @RomeenSheth @jspujji @alexisohanian It’s how I’ve gotten 8-figures worth of advertisers at @MorningBrew . @MorningBrew And it’s how I’ve worked through business ideas, company challenges, & thoughts on where the world is going. At the same time, I knew most founders didn’t have the chops or the time to create great content, solo on social. Which led to a literal shower thought. What if I pair founders with world-class strategists & copywriters. A strategist would pull unique insights from the founder & help set their social strategy. A copywriter would translate ideas into sharp content. Seems logical, right? Personal brand is a nice-to-have, not a need-to-have for founders. The result was 5-6 months of smooth sailing with a client followed by the gutpunch… “Can we catch up?” “Can we catch up?” Churn for exec social became unpalatable. It was driven by: Founders becoming impatient & expecting results sooner. Founders being pulled in 50 directions & lacking time for social content. Founders feeling like their internet voice was slightly different from their real life voice. Founders becoming impatient & expecting results sooner. Founders being pulled in 50 directions & lacking time for social content. Founders feeling like their internet voice was slightly different from their real life voice. My cofounder, @AbbyMurray , and I knew that we had to get off the hamster wheel if we wanted to build a $10m biz. So we did two things: @AbbyMurray First, we asked ourselves: “What unfair advantages do we have as a business today?” “The best writers & strategists on the internet, period. We have better ideas & execution.” Second, we listened to our customers (and honestly, no one else). We reflected on past convos: A B2B HR Tech co asked if we could write their newsletter An e-comm SaaS founder asked for help with e-books A CMO wanted a company social revival A B2B HR Tech co asked if we could write their newsletter An e-comm SaaS founder asked for help with e-books A CMO wanted a company social revival We also interviewed the shit out of our customers. We asked them if they had any other content needs. We asked their marketing teams about their biggest current challenges. We asked “if you had access to world-class writers/content minds, how would you use them?” The answer (and our pivot) became clear. Founders kinda need content help. B2B companies reallyyyy need content help. Marketing teams are scrambling to stand out & grow their business today. Marketing costs are rising (adios cookies, bye zero interest rates, hello increased competition) Product differentiation is narrowing (software + AI begets more software + AI) Attribution has become religion, which forces marketers into a box of undifferentiated content Most brands are living in a sea of sameness (groupthink + AI tooling) Headcount is recalibrating (we no longer live in pre-COVID La La Land) Marketing costs are rising (adios cookies, bye zero interest rates, hello increased competition) Product differentiation is narrowing (software + AI begets more software + AI) Attribution has become religion, which forces marketers into a box of undifferentiated content Most brands are living in a sea of sameness (groupthink + AI tooling) Headcount is recalibrating (we no longer live in pre-COVID La La Land) Which means every marketer & every company is looking to stand out creatively & acquire customers cheaply & scalably. High-quality, high-value content that drives demand is one of the answers. But marketing orgs aren’t equipped to build & execute stand-out content strategies for 5 main reasons: They don’t know how to hire great content talent (copywriters, video creators, producers, strategists), because they’ve never had to do so. They have marketers doing a lot of the content work, but are starting to realize it’s a completely different skill from most marketing responsibilities. They have 1-2 team members creating content, but it feels flat & they have no idea what the hell to do now. They do a poor job of tracking performance & connecting content to pipeline. Content isn’t centralized...so it’s good in some places, fine in others, and f*cking atrocious elsewhere. They don’t know how to hire great content talent (copywriters, video creators, producers, strategists), because they’ve never had to do so. They have marketers doing a lot of the content work, but are starting to realize it’s a completely different skill from most marketing responsibilities. They have 1-2 team members creating content, but it feels flat & they have no idea what the hell to do now. They do a poor job of tracking performance & connecting content to pipeline. Content isn’t centralized...so it’s good in some places, fine in others, and f*cking atrocious elsewhere. Today, storyarb is your outsourced Head of Content Marketing, helping to resuscitate your company’s lifeless content (no matter which situation you’re in). It starts with us understanding the state of your content today. This includes a literal content colonoscopy up & down the org: Newsletters Email marketing Company social Founder social Ad creative Website copy Blog posts Lead magnets SOPs/technical docs [FILL IN THE BLANK] Newsletters Email marketing Company social Founder social Ad creative Website copy Blog posts Lead magnets SOPs/technical docs [FILL IN THE BLANK] Next, our strategists & writers work in lockstep to UNF*CK YOUR CONTENT by answering one crucial question: “What strategy (read: channels & ideas) will allow us to drive scalable customer demand without sacrificing the quality of our content?” “What strategy (read: channels & ideas) will allow us to drive scalable customer demand without sacrificing the quality of our content?” That was the pivot. And it’s working in a big way. Series C tech startups to publicly traded multinationals trust us to be their Head of Content Marketing & we’re driving pipeline with high-quality, niche content built for our customers’ ICP. A few final notes as I reflect on the experience & journey building @storyarb alongside Abby over the last year. @storyarb Pivots are basically inevitable. The odds that your day 1 product is your day 365 product is close to zero. It’s impossible to make perfect assumptions pre-launch & what’s most important is how you sift through info from your customers & make intuitive & informed decisions thereafter. There is a business to build around founder social, it’s just not a recurring revenue agency. If I was to build a founder/exec social-only business again, it’d be a combo of courses (organized by platform & expertise), corporate workshops & paid community (accountability/engagement groups). Building a great business is far more important than being right. As a first-time founder, you want to stay the course & protect your ego. As a second-time founder, you want to make adjustments & build a strong, dependable business. Great talent creates great competencies. Great competencies create options. The only reason we’ve been able to evolve storyarb & crush it for our current clients is because the business was built on a foundation of exceptional talent. Pivots are basically inevitable. The odds that your day 1 product is your day 365 product is close to zero. It’s impossible to make perfect assumptions pre-launch & what’s most important is how you sift through info from your customers & make intuitive & informed decisions thereafter. Pivots are basically inevitable. The odds that your day 1 product is your day 365 product is close to zero. It’s impossible to make perfect assumptions pre-launch & what’s most important is how you sift through info from your customers & make intuitive & informed decisions thereafter. There is a business to build around founder social, it’s just not a recurring revenue agency. If I was to build a founder/exec social-only business again, it’d be a combo of courses (organized by platform & expertise), corporate workshops & paid community (accountability/engagement groups). There is a business to build around founder social, it’s just not a recurring revenue agency. If I was to build a founder/exec social-only business again, it’d be a combo of courses (organized by platform & expertise), corporate workshops & paid community (accountability/engagement groups). Building a great business is far more important than being right. As a first-time founder, you want to stay the course & protect your ego. As a second-time founder, you want to make adjustments & build a strong, dependable business. Building a great business is far more important than being right. As a first-time founder, you want to stay the course & protect your ego. As a second-time founder, you want to make adjustments & build a strong, dependable business. Great talent creates great competencies. Great competencies create options. The only reason we’ve been able to evolve storyarb & crush it for our current clients is because the business was built on a foundation of exceptional talent. Great talent creates great competencies. Great competencies create options. The only reason we’ve been able to evolve storyarb & crush it for our current clients is because the business was built on a foundation of exceptional talent. I hope this post made pivoting feel a little less like a failure to you. And if you’re a marketer, I hope this post made you feel like you don’t have to settle for mediocre content, while growing your business.

Why storyarb Pivoted From Ghostwriting for Founders to Producing Niche Content for Customers’ ICP

Proč storyarb přešel od Ghostwritingu pro zakladatele k vytváření specializovaného obsahu pro ICP zákazníků

Por qué Storyarb pasó de ser redactora fantasma para fundadores a producir contenido de nicho para el ICP de los clientes

Miksi storyarb siirtyi haamukirjoituksesta perustajille niche-sisällön tuottamiseen asiakkaiden ICP:lle

Mengapa storyarb Beralih Dari Ghostwriting untuk Pendiri ke Produksi Konten Niche untuk Pelanggan

storyarb が創業者向けのゴーストライター業務から顧客の ICP 向けニッチ コンテンツの制作に方向転換した理由

Storyarb가 창업자를 위한 고스트라이터에서 고객의 ICP를 위한 틈새 콘텐츠 제작으로 전환한 이유

Mpo na nini storyarb Pivoted De Ghostwriting mpo na ba Fondateurs mpo na kobimisa Niche Content mpo na Clients’ ICP

Nahoana ny storyarb no nivadika avy amin'ny Ghostwriting ho an'ny mpanorina mba hamokarana votoaty manokana ho an'ny ICP mpanjifa

Зошто storyarb се сврти од Ghostwriting за основачи до производство на ниша содржина за ICP на клиентите

किन स्टोरीआर्ब संस्थापकहरूको लागि भूतलेखनबाट ग्राहकहरूको आईसीपीको लागि आला सामग्री उत्पादन गर्न पिभोट गरियो

Чаро storyarb аз Ghostwriting барои муассисон ба истеҳсоли мундариҷаи нишеӣ барои ICP муштариён табдил ёфт

Чому storyarb перейшов від Ghostwriting для засновників до створення нішевого контенту для ICP клієнтів

Optimizing workflows with AI has become a game-changer for me, especially when it comes to searching for information. For a while, I relied on the free version of Perplexity, but recently, I upgraded to Perplexity Pro. Meanwhile, as a longtime user of ChatGPT, I’ve also been exploring its new ChatGPT Search feature. In this article, I’ll share my experiences with both tools, compare their strengths and weaknesses, and help you decide which one might be the better fit for your needs. Perplexity I’d heard about Perplexity.ai many times, promised myself I’d try it, and eventually did, but it didn’t quite click. It’s hard to fully appreciate a tool until you use it regularly. I forgot about it until I got a test assignment for a job interview in a new field, requiring tons of research. Honestly, I’m not great at googling. But since I frequently use ChatGPT and have gotten pretty good at writing prompts, I thought of giving Perplexity another shot. This was before the release of ChatGPT Search, and at that point, I’d already maxed out what ChatGPT could offer. Perplexity turned out to be a lifesaver. It made long, tedious searches much quicker and was my first truly game-changing experience with AI search. At the time, I used the free version. Compared to browser searches, here’s what I found more convenient in AI search: Fewer distractions from ads: Most browsers put ads at the top, so you have to scroll past them just to get to the first actual search result. Even without the ads, the top links are often not very relevant, and there are still too many to sift through. This makes it easy to lose motivation to click beyond the first couple of pages. AI does all that for you, providing a list of sources you can explore further on your own.


Threaded searches with context: I loved how searches are organized into threads, preserving context. For my test assignment, since I was learning a new field from scratch, one question would lead to many others. It was convenient to continue and expand the search without starting over each time. If I remembered something later, I could easily return to a specific thread and pick up where I left off.


Helpful question suggestions: Each thread in Perplexity includes prompts for related questions, which makes navigating a new topic much easier.


Incognito mode: Like most browsers, Perplexity offers an incognito mode. I haven’t used it yet, but it’s a nice feature.


Language flexibility: If you write in a certain language, you’ll get sources in that language. This is a standard approach for browsers but not always convenient. For a while, I continued using the free version, but recently I switched to Pro. The free version uses the GPT-3.5 model, whereas the Pro plan grants access to the latest models: GPT-4 Omni, Claude-3 Sonnet/Opus, and Sonar Large 32k, so all the best features are integrated into your search. Of course, the Pro version offers better, smarter, and faster search. In addition to the features mentioned above, here’s what I found most useful: Broaden your search with images: Pro provides access to Playground V2.5, DALLE 3, and Stable Diffusion XL models that can expand your search. For example, I was recently researching fund flow modeling, and having visual examples was incredibly helpful.


Files, code, and more input options: You can upload unlimited files, code, pictures, etc. It’s really fast and much more convenient than doing this through a standard browser because it’s all in one space with seamless AI interaction. ChatGPT Search This feature was launched in October 2024 and is based on GPT-4o, specifically trained for search. It’s integrated into ChatGPT, allowing you to enable it with a Search button or simply ask ChatGPT to search the web. As I mentioned, I’ve been a long-time user of ChatGPT for several years, so I seamlessly integrated the search feature into my routine. Since free Perplexity uses GPT-3.5, all its features like AI search with no ads, no endless scrolling, and results organized into threads with helpful tips are available here as well. My favorite feature here is its accessibility, offering text-to-speech, and the ability to create queries via audio, and audio dialogs. Choose your fighter Search Quality Perplexity excels at finding information, while ChatGPT Search is better at explaining it. From a user perspective, I wouldn’t say I’ve encountered any critical issues with not finding something. It’s just a matter of different approaches. Perplexity provides more an overview of sources, whereas ChatGPT tends to give more explanation than summary. Generated Answers Without Sources Both tools provide sources for their answers, but sometimes the information might be generated without a reliable link. Even when a link is provided, the relevant information might not actually be there. It’s always better to double-check and read the sources yourself. Based on my experience, ChatGPT is more prone to generating unsupported content than Perplexity, but I’ve encountered this issue in both tools. Search Speed I didn’t notice a significant difference between the two in terms of speed. However, both are slower than a standard browser search. Chrome extensions Both tools offer Chrome extensions, and I tested both. The ChatGPT extension lets you use ChatGPT as your primary search engine, replacing, for example, Google. When you enter something in the search bar, it redirects you to ChatGPT, where your query is processed. Honestly, I wasn’t a fan. It’s slower than Google, and you can feel it. I still prefer a traditional search bar for quick lookups. Perplexity’s Chrome extension works differently. Instead of replacing your search engine, it opens a small window over the current page, allowing you to googling and query Perplexity simultaneously. I really loved this one, because it’s more useful for me. The drawback is that you can only enter one query at a time, and if you want to continue the conversation, it redirects you to a separate Perplexity tab. Unique Features Since both tools use GPT models, they share many great features. Perplexity Pro stands out with its use of multiple models and built-in illustrations. On the other hand, I really appreciate ChatGPT’s accessibility and seamless integration into its apps, which I use for more than just search. Bonus: Hangman game test I saw a funny tweet claiming ChatGPT can’t play Hangman. Turns out, neither can Perplexity Pro, even with all its models. Final Take: Which Works Best for What To start with, I’m a fan of AI search. Usually, it takes longer than quick googling, so here’s how I see it: if I need a super quick answer from the first link, I’ll use a standard browser search. But if I need to find more information or conduct deeper research, AI search is the way to go. It’s the best method to uncover valuable insights more efficiently. As for ChatGPT Search and Perplexity, in my experience, they solve different problems. ChatGPT Search perfectly extends ChatGPT’s capabilities, given that ChatGPT’s knowledge base only goes up to the year 2021. It’s perfect for studying and gaining insights using up-to-date web information. Perplexity, on the other hand, is better for finding something more thoroughly. It takes more from browser search, like incognito pages, and its approach to work with your prompts. It is about searching for information first and explaining second, in contrast to ChatGPT Search. For me: Need to find something? Perplexity.
Need to understand something? ChatGPT + Search. They complement each other perfectly in work. Optimizing workflows with AI has become a game-changer for me, especially when it comes to searching for information. For a while, I relied on the free version of Perplexity , but recently, I upgraded to Perplexity Pro . Meanwhile, as a longtime user of ChatGPT , I’ve also been exploring its new ChatGPT Search feature. In this article, I’ll share my experiences with both tools, compare their strengths and weaknesses, and help you decide which one might be the better fit for your needs. Perplexity Perplexity Pro ChatGPT ChatGPT Search Perplexity I’d heard about Perplexity.ai many times, promised myself I’d try it, and eventually did, but it didn’t quite click. It’s hard to fully appreciate a tool until you use it regularly. I forgot about it until I got a test assignment for a job interview in a new field, requiring tons of research. Honestly, I’m not great at googling. But since I frequently use ChatGPT and have gotten pretty good at writing prompts, I thought of giving Perplexity another shot. This was before the release of ChatGPT Search, and at that point, I’d already maxed out what ChatGPT could offer. Perplexity turned out to be a lifesaver. It made long, tedious searches much quicker and was my first truly game-changing experience with AI search. Perplexity.ai At the time, I used the free version. Compared to browser searches, here’s what I found more convenient in AI search: Fewer distractions from ads: Most browsers put ads at the top, so you have to scroll past them just to get to the first actual search result. Even without the ads, the top links are often not very relevant, and there are still too many to sift through. This makes it easy to lose motivation to click beyond the first couple of pages. AI does all that for you, providing a list of sources you can explore further on your own. Threaded searches with context: I loved how searches are organized into threads, preserving context. For my test assignment, since I was learning a new field from scratch, one question would lead to many others. It was convenient to continue and expand the search without starting over each time. If I remembered something later, I could easily return to a specific thread and pick up where I left off. Helpful question suggestions: Each thread in Perplexity includes prompts for related questions, which makes navigating a new topic much easier. Incognito mode: Like most browsers, Perplexity offers an incognito mode. I haven’t used it yet, but it’s a nice feature. Language flexibility: If you write in a certain language, you’ll get sources in that language. This is a standard approach for browsers but not always convenient. Fewer distractions from ads: Most browsers put ads at the top, so you have to scroll past them just to get to the first actual search result. Even without the ads, the top links are often not very relevant, and there are still too many to sift through. This makes it easy to lose motivation to click beyond the first couple of pages. AI does all that for you, providing a list of sources you can explore further on your own. Fewer distractions from ads: Most browsers put ads at the top, so you have to scroll past them just to get to the first actual search result. Even without the ads, the top links are often not very relevant, and there are still too many to sift through. This makes it easy to lose motivation to click beyond the first couple of pages. AI does all that for you, providing a list of sources you can explore further on your own. Fewer distractions from ads: Threaded searches with context: I loved how searches are organized into threads, preserving context. For my test assignment, since I was learning a new field from scratch, one question would lead to many others. It was convenient to continue and expand the search without starting over each time. If I remembered something later, I could easily return to a specific thread and pick up where I left off. Threaded searches with context: I loved how searches are organized into threads, preserving context. For my test assignment, since I was learning a new field from scratch, one question would lead to many others. It was convenient to continue and expand the search without starting over each time. If I remembered something later, I could easily return to a specific thread and pick up where I left off. Threaded searches with context: Helpful question suggestions: Each thread in Perplexity includes prompts for related questions, which makes navigating a new topic much easier. Helpful question suggestions: Each thread in Perplexity includes prompts for related questions, which makes navigating a new topic much easier. Helpful question suggestions: Incognito mode: Like most browsers, Perplexity offers an incognito mode. I haven’t used it yet, but it’s a nice feature. Incognito mode: Like most browsers, Perplexity offers an incognito mode. I haven’t used it yet, but it’s a nice feature. Incognito mode: Language flexibility: If you write in a certain language, you’ll get sources in that language. This is a standard approach for browsers but not always convenient. Language flexibility: If you write in a certain language, you’ll get sources in that language. This is a standard approach for browsers but not always convenient. Language flexibility: For a while, I continued using the free version, but recently I switched to Pro . The free version uses the GPT-3.5 model, whereas the Pro plan grants access to the latest models: GPT-4 Omni, Claude-3 Sonnet/Opus, and Sonar Large 32k, so all the best features are integrated into your search. Of course, the Pro version offers better, smarter, and faster search. Pro In addition to the features mentioned above, here’s what I found most useful: In addition to the features mentioned above, here’s what I found most useful: Broaden your search with images: Pro provides access to Playground V2.5, DALLE 3, and Stable Diffusion XL models that can expand your search. For example, I was recently researching fund flow modeling, and having visual examples was incredibly helpful. Files, code, and more input options: You can upload unlimited files, code, pictures, etc. It’s really fast and much more convenient than doing this through a standard browser because it’s all in one space with seamless AI interaction. Broaden your search with images: Pro provides access to Playground V2.5, DALLE 3, and Stable Diffusion XL models that can expand your search. For example, I was recently researching fund flow modeling, and having visual examples was incredibly helpful. Broaden your search with images: Pro provides access to Playground V2.5, DALLE 3, and Stable Diffusion XL models that can expand your search. For example, I was recently researching fund flow modeling, and having visual examples was incredibly helpful. Broaden your search with images: Files, code, and more input options: You can upload unlimited files, code, pictures, etc. It’s really fast and much more convenient than doing this through a standard browser because it’s all in one space with seamless AI interaction. Files, code, and more input options: You can upload unlimited files, code, pictures, etc. It’s really fast and much more convenient than doing this through a standard browser because it’s all in one space with seamless AI interaction. Files, code, and more input options: ChatGPT Search ChatGPT Search This feature was launched in October 2024 and is based on GPT-4o, specifically trained for search. It’s integrated into ChatGPT , allowing you to enable it with a Search button or simply ask ChatGPT to search the web. ChatGPT As I mentioned, I’ve been a long-time user of ChatGPT for several years, so I seamlessly integrated the search feature into my routine. Since free Perplexity uses GPT-3.5, all its features like AI search with no ads, no endless scrolling, and results organized into threads with helpful tips are available here as well. My favorite feature here is its accessibility, offering text-to-speech, and the ability to create queries via audio, and audio dialogs. Choose your fighter Search Quality Search Quality Search Quality Search Quality Perplexity excels at finding information, while ChatGPT Search is better at explaining it. From a user perspective, I wouldn’t say I’ve encountered any critical issues with not finding something. It’s just a matter of different approaches. Perplexity provides more an overview of sources, whereas ChatGPT tends to give more explanation than summary. overview explanation Generated Answers Without Sources Generated Answers Without Sources Generated Answers Without Sources Generated Answers Without Sources Both tools provide sources for their answers, but sometimes the information might be generated without a reliable link. Even when a link is provided, the relevant information might not actually be there. It’s always better to double-check and read the sources yourself. Based on my experience, ChatGPT is more prone to generating unsupported content than Perplexity, but I’ve encountered this issue in both tools. Search Speed Search Speed Search Speed Search Speed I didn’t notice a significant difference between the two in terms of speed. However, both are slower than a standard browser search. Chrome extensions Chrome extensions Chrome extensions Chrome extensions Both tools offer Chrome extensions, and I tested both. The ChatGPT extension lets you use ChatGPT as your primary search engine, replacing, for example, Google. When you enter something in the search bar, it redirects you to ChatGPT, where your query is processed. Honestly, I wasn’t a fan. It’s slower than Google, and you can feel it. I still prefer a traditional search bar for quick lookups. Perplexity’s Chrome extension works differently. Instead of replacing your search engine, it opens a small window over the current page, allowing you to googling and query Perplexity simultaneously. I really loved this one, because it’s more useful for me. The drawback is that you can only enter one query at a time, and if you want to continue the conversation, it redirects you to a separate Perplexity tab. Unique Features Unique Features Unique Features Unique Features Since both tools use GPT models, they share many great features. Perplexity Pro stands out with its use of multiple models and built-in illustrations. On the other hand, I really appreciate ChatGPT’s accessibility and seamless integration into its apps, which I use for more than just search. Bonus: Hangman game test Bonus: Hangman game test Bonus: Hangman game test Bonus: Hangman game test I saw a funny tweet claiming ChatGPT can’t play Hangman. Turns out, neither can Perplexity Pro, even with all its models. Final Take: Which Works Best for What To start with, I’m a fan of AI search. Usually, it takes longer than quick googling, so here’s how I see it: if I need a super quick answer from the first link, I’ll use a standard browser search. But if I need to find more information or conduct deeper research, AI search is the way to go. It’s the best method to uncover valuable insights more efficiently. As for ChatGPT Search and Perplexity, in my experience, they solve different problems. ChatGPT Search perfectly extends ChatGPT’s capabilities, given that ChatGPT’s knowledge base only goes up to the year 2021. It’s perfect for studying and gaining insights using up-to-date web information. Perplexity, on the other hand, is better for finding something more thoroughly. It takes more from browser search, like incognito pages, and its approach to work with your prompts. It is about searching for information first and explaining second, in contrast to ChatGPT Search. For me: For me: Need to find something? Perplexity. Need to understand something? ChatGPT + Search. Need to find something? Perplexity. Need to find something? Need to understand something? ChatGPT + Search. Need to understand something? They complement each other perfectly in work.

Perplexity Pro vs. ChatGPT Search: Which AI Tool is Best Fits Your Workflow Needs?

Perplexity супраць пошуку ChatGPT: які інструмент AI лепш за ўсё адпавядае вашым патрэбам працоўнага працэсу?

Perplexity срещу ChatGPT Search: Кой AI инструмент отговаря най-добре на вашите нужди от работен процес?

Perplexity vs. Búsqueda en ChatGPT: ¿Qué herramienta de IA se adapta mejor a sus necesidades de flujo de trabajo?

Շփոթմունքն ընդդեմ ChatGPT որոնման. Ո՞ր AI գործիքն է լավագույնս համապատասխանում ձեր աշխատանքային հոսքի կարիքներին:

Perplexity と ChatGPT Search: どちらの AI ツールがワークフローのニーズに最適ですか?

ភាពច្របូកច្របល់ធៀបនឹងការស្វែងរក ChatGPT៖ តើឧបករណ៍ AI មួយណាដែលសាកសមបំផុតនឹងតម្រូវការលំហូរការងាររបស់អ្នក?

Perplexity vs. ChatGPT paieška: kuris AI įrankis geriausiai atitinka jūsų darbo eigos poreikius?

Fikarohana ChatGPT: Inona no fitaovana AI mifanaraka amin'ny filanao?

Збунетост наспроти Пребарување ChatGPT: која алатка за вештачка интелигенција најдобро одговара на вашите потреби на работниот тек?

Urujijo na ChatGPT Gushakisha: Niki Gikoresho cya AI gihuye neza nakazi kawe gakenewe?

குழப்பம் மற்றும் ChatGPT தேடல்: எந்த AI கருவி உங்கள் பணிப்பாய்வு தேவைகளுக்கு மிகவும் பொருத்தமானது?

Perplexity กับ ChatGPT Search: เครื่องมือ AI ตัวใดที่เหมาะกับความต้องการเวิร์กโฟลว์ของคุณที่สุด

The art of prompt engineering is becoming a critical skill for effectively harnessing AI's potential as artificial intelligence continues to evolve. Yet, for all its importance, prompt engineering is often reduced to simplistic advice—tips like “be specific” or quick hacks such as “let’s think step by step.” While helpful for beginners, these pointers barely scratch the surface. True prompt engineering requires a much deeper understanding of the mechanics behind AI responses, moving far beyond what’s often little more than zero-shot Google searching. Here we go through some foundational principles to explain why good prompting actually works. Embeddings: The Foundation of Prompt Understanding One of the foundational concepts in effective prompting is understanding embeddings. When we input a prompt into a model, it doesn’t process language as we do; instead, each word or phrase is transformed into an embedding, a numerical representation that captures both the meaning and context of the language. These embeddings act as the model’s internal mapping, helping it interpret relationships, nuances, and associations between words. For prompt engineers, understanding embeddings is essential for prompt intuition. The way a word is represented in embedding space determines how the model interprets it, affecting the content and focus of responses. For example, if a prompt requests information on “manufacturing,” the model will pull content based on embeddings linked to those terms, potentially drawing from areas like production processes, supply chain logistics, or technological advancements. A prompt engineer can influence these responses by skillfully crafting the wording, improving the relevance and depth of the output. Understanding embeddings also empowers prompt engineers to chain prompts better, where each response builds on previous interactions. By crafting sequences with embedding relationships in mind, prompt engineers create complex, context-aware conversations that adapt to specific needs, producing detailed, responsive outputs. The Role of Attention: Guiding Model Focus Another core component in prompt engineering is attention. Unlike human attention, which can dynamically shift between different topics, AI attention assigns weight to relationships between words within the prompt and response text. Attention helps the model prioritize certain parts of the input, and a well-crafted prompt uses this to shape the model’s focus. Take, for instance, a prompt asking for a nuanced view of a cat sitting on a mat. Prompt engineers can guide the model to emphasize relevant aspects of the prompt's subject, such as the cat's posture, the texture and color of the mat, or the overall scene’s ambiance, leading to a more detailed and vivid response. This approach to prompting. known as directional stimulus, allows the extraction of richer, more relevant answers from the model. RAG: Retrieval-Augmented Generation for Enhanced Responses Retrieval-Augmented Generation (RAG) combines generative AI with retrieval systems to improve the accuracy and relevance of responses. Traditional prompt engineering relies on a model’s internal knowledge, which can be outdated or incomplete. RAG addresses this by adding a retrieval layer that pulls in current, contextually relevant information from external sources like databases or search engines, enabling the model to provide more accurate, up-to-date answers. RAG is transformative for prompt engineering. It allows engineers to structure prompts that direct both the retrieval and generative components to work together, especially valuable for complex or fast-evolving subjects. For instance, a prompt about “recent changes in data privacy laws” could trigger the model to fetch the latest legal documents or news, which it then combines with internal information via RAG to synthesize into a coherent response. This dual capability reduces hallucinations and keeps the model grounded in real-time, reliable information. Prompt engineers working within RAG frameworks can therefore create interactions that are both innovative and anchored in current knowledge, adding an extra layer of sophistication to the AI’s output. Image Prompting and the Diffusion Process In image generation, prompt engineering takes on another layer of complexity. Here, prompts are not just about words but about guiding a model through the diffusion process—a technique where random noise is iteratively transformed into a coherent image. The diffusion process relies on iterative adjustments, and each cycle builds upon the previous one to refine an image based on the prompt's guidance. When crafting prompts for image generation, understanding diffusion is essential. Descriptions like “award winning photography” or “fashion photography” can yield varying results depending on how the model interprets "award winning" or “fashion.” Prompt engineers must understand the model’s interpretation of spatial relationships, color, and composition to produce a desired outcome. It’s about more than aesthetics; it’s about leading the model through each step to align with the intended visual concept. Hallucinations and the Pitfalls of Poor Prompting One of the most challenging aspects of AI prompting is managing hallucinations—when a model produces content that sounds plausible but is entirely fabricated. Hallucinations often result from vague or poorly structured prompts, which leave too much to the model’s interpretation. In fields like healthcare or finance, where accuracy is critical, even a small hallucination can have serious consequences. Skilled prompt engineers prevent this by constructing prompts that minimize ambiguity, verifying outputs with fact-checking prompts, and maintaining strict clarity and focus. Why Prompt Engineering is a Real Skill Ultimately, prompt engineering is a genuine skill that combines language intuition with a deep technical understanding of AI mechanics. Unlike simple search queries, prompt engineers with knowledge of embeddings, attention mechanisms, and model limitations are able to craft better prompts. Mastering these elements enables prompt engineers to shape responses that are precise, reliable, and often surprisingly creative. The need for skilled prompt engineers will only grow as AI technology continues to integrate into industries from education to entertainment. They’ll be essential not just for generating content, but for refining AI applications, troubleshooting outputs, and optimizing model performance across fields. Far from a passing trend, prompt engineering is shaping up to be one of the defining skills of the AI era—an essential tool for building communication between humans and machines. About Me: 25+ year IT veteran combining data, AI, risk management, strategy, and education. 4x global hackathon winner and social impact from data advocate. Currently working to jumpstart the AI workforce in the Philippines. Learn more about me here. The art of prompt engineering is becoming a critical skill for effectively harnessing AI's potential as artificial intelligence continues to evolve. Yet, for all its importance, prompt engineering is often reduced to simplistic advice—tips like “be specific” or quick hacks such as “let’s think step by step.” While helpful for beginners, these pointers barely scratch the surface. True prompt engineering requires a much deeper understanding of the mechanics behind AI responses, moving far beyond what’s often little more than zero-shot Google searching. Here we go through some foundational principles to explain why good prompting actually works. Embeddings: The Foundation of Prompt Understanding One of the foundational concepts in effective prompting is understanding embeddings. When we input a prompt into a model, it doesn’t process language as we do; instead, each word or phrase is transformed into an embedding, a numerical representation that captures both the meaning and context of the language. These embeddings act as the model’s internal mapping, helping it interpret relationships, nuances, and associations between words. a numerical representation For prompt engineers, understanding embeddings is essential for prompt intuition. The way a word is represented in embedding space determines how the model interprets it, affecting the content and focus of responses. For example, if a prompt requests information on “manufacturing,” the model will pull content based on embeddings linked to those terms, potentially drawing from areas like production processes, supply chain logistics, or technological advancements. A prompt engineer can influence these responses by skillfully crafting the wording, improving the relevance and depth of the output. Understanding embeddings also empowers prompt engineers to chain prompts better , where each response builds on previous interactions. By crafting sequences with embedding relationships in mind, prompt engineers create complex, context-aware conversations that adapt to specific needs, producing detailed, responsive outputs. chain prompts better The Role of Attention: Guiding Model Focus Another core component in prompt engineering is attention. Unlike human attention, which can dynamically shift between different topics, AI attention assigns weight to relationships between words within the prompt and response text. Attention helps the model prioritize certain parts of the input, and a well-crafted prompt uses this to shape the model’s focus. Take, for instance, a prompt asking for a nuanced view of a cat sitting on a mat. relationships between words Prompt engineers can guide the model to emphasize relevant aspects of the prompt's subject, such as the cat's posture, the texture and color of the mat, or the overall scene’s ambiance, leading to a more detailed and vivid response. This approach to prompting. known as directional stimulus , allows the extraction of richer, more relevant answers from the model. directional stimulus RAG: Retrieval-Augmented Generation for Enhanced Responses Retrieval-Augmented Generation (RAG) combines generative AI with retrieval systems to improve the accuracy and relevance of responses. Traditional prompt engineering relies on a model’s internal knowledge, which can be outdated or incomplete. RAG addresses this by adding a retrieval layer that pulls in current, contextually relevant information from external sources like databases or search engines, enabling the model to provide more accurate, up-to-date answers. adding a retrieval layer RAG is transformative for prompt engineering. It allows engineers to structure prompts that direct both the retrieval and generative components to work together, especially valuable for complex or fast-evolving subjects. For instance, a prompt about “recent changes in data privacy laws” could trigger the model to fetch the latest legal documents or news, which it then combines with internal information via RAG to synthesize into a coherent response. This dual capability reduces hallucinations and keeps the model grounded in real-time, reliable information. Prompt engineers working within RAG frameworks can therefore create interactions that are both innovative and anchored in current knowledge, adding an extra layer of sophistication to the AI’s output. Image Prompting and the Diffusion Process In image generation, prompt engineering takes on another layer of complexity. Here, prompts are not just about words but about guiding a model through the diffusion process—a technique where random noise is iteratively transformed into a coherent image. The diffusion process relies on iterative adjustments, and each cycle builds upon the previous one to refine an image based on the prompt's guidance. random noise is iteratively transformed When crafting prompts for image generation, understanding diffusion is essential. Descriptions like “award winning photography” or “fashion photography” can yield varying results depending on how the model interprets "award winning" or “fashion.” Prompt engineers must understand the model’s interpretation of spatial relationships, color, and composition to produce a desired outcome. It’s about more than aesthetics; it’s about leading the model through each step to align with the intended visual concept. Hallucinations and the Pitfalls of Poor Prompting One of the most challenging aspects of AI prompting is managing hallucinations—when a model produces content that sounds plausible but is entirely fabricated. Hallucinations often result from vague or poorly structured prompts , which leave too much to the model’s interpretation. In fields like healthcare or finance, where accuracy is critical, even a small hallucination can have serious consequences. vague or poorly structured prompts Skilled prompt engineers prevent this by constructing prompts that minimize ambiguity , verifying outputs with fact-checking prompts, and maintaining strict clarity and focus. prompts that minimize ambiguity Why Prompt Engineering is a Real Skill Ultimately, prompt engineering is a genuine skill that combines language intuition with a deep technical understanding of AI mechanics. Unlike simple search queries, prompt engineers with knowledge of embeddings, attention mechanisms, and model limitations are able to craft better prompts. Mastering these elements enables prompt engineers to shape responses that are precise, reliable, and often surprisingly creative. The need for skilled prompt engineers will only grow as AI technology continues to integrate into industries from education to entertainment. They’ll be essential not just for generating content, but for refining AI applications, troubleshooting outputs, and optimizing model performance across fields. Far from a passing trend, prompt engineering is shaping up to be one of the defining skills of the AI era—an essential tool for building communication between humans and machines. About Me: 25+ year IT veteran combining data, AI, risk management, strategy, and education. 4x global hackathon winner and social impact from data advocate. Currently working to jumpstart the AI workforce in the Philippines. Learn more about me here . here

Explaining Prompt Engineering

প্রম্পট ইঞ্জিনিয়ারিং ব্যাখ্যা করা

Erklärung zu Prompt Engineering

Explicando la ingeniería rápida

Explication de l'ingénierie rapide

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プロンプトエンジニアリングの説明

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Search engines dominate information retrieval, but iAsk AI is redefining what’s possible. In a groundbreaking achievement on the GPQA Diamond benchmark, iAsk AI’s advanced model, iAsk Pro, has set new records in accuracy for complex, graduate-level scientific problem-solving. This isn’t just a technical milestone — it’s a reimagining of how AI can understand, process, and answer challenging questions with human-like depth and precision. What is the GPQA Benchmark? GPQA (Graduate-Level Google-Proof Q&A Benchmark) is one of the most rigorous tests for AI models, designed to challenge them in fields like biology, physics, and chemistry. These are not typical questions; they demand knowledge and nuanced, multi-step reasoning that can stump even PhD-level experts. Remarkably, iAsk Pro achieved a record-breaking 78.28% accuracy on the GPQA Diamond subset — comprising the benchmark’s most challenging 198 questions — outperforming leading models like OpenAI’s GPT and Anthropic’s Claude 3.5. This accomplishment sets a new standard in AI’s capacity to tackle the toughest, most intricate queries. Unlike general benchmarks, GPQA focuses on “Google-proof” questions that resist simple answers. These questions require advanced reasoning, the kind that rivals human experts. The complexity is so high that even specialized professionals typically average around 65% accuracy. iAsk Pro’s breakthrough accuracy reflects its unique ability to mirror the depth of human cognitive processing, setting it apart in the AI landscape. How iAsk AI Achieves Unmatched Accuracy Unlike standard search engines that rely heavily on keyword matching, iAsk Pro’s approach goes far deeper. It uses Chain of Thought (CoT) reasoning to deconstruct intricate, multi-layered questions step by step. This method mirrors human logic, enabling iAsk Pro to deliver responses that are both highly accurate and contextually relevant. Users receive well-rounded, clear answers instead of vague references, underscoring iAsk Pro’s dedication to precision. Unlike standard search engines that rely heavily on keyword matching, iAsk Pro’s approach goes far deeper. It uses Chain of Thought (CoT) reasoning to deconstruct intricate, multi-layered questions step by step. This method mirrors human logic, enabling iAsk Pro to deliver responses that are both highly accurate and contextually relevant. Users receive well-rounded, clear answers instead of vague references, underscoring iAsk Pro’s dedication to precision. The GPQA benchmark was specifically designed to test AI models beyond surface-level knowledge, demanding advanced reasoning. iAsk’s choice to focus on this challenging benchmark was strategic, showcasing its capabilities in fields like academia, research, and other data-driven domains. With its high GPQA accuracy, iAsk Pro is poised to drive breakthroughs in areas that require deep scientific insight, establishing itself as an invaluable resource in advanced knowledge fields. The Future of AI-Driven Knowledge with iAsk Pro For professionals, academics, and anyone who values precision, iAsk Pro heralds a new era of AI-powered inquiry. Its record-breaking performance points toward a future where technology not only aids information retrieval but actively advances collective understanding. From supporting scientific discoveries to offering users a reliable source of accurate knowledge, iAsk AI is reshaping the role of search technology in our lives. iAsk Pro’s success represents a step toward AI that can work alongside individuals as a problem-solver, capable of addressing the depth and complexity of human inquiry. This article is published under HackerNoon’s Business Blogging program. Learn more about the program here. Search engines dominate information retrieval, but iAsk AI is redefining what’s possible. In a groundbreaking achievement on the GPQA Diamond benchmark, iAsk AI’s advanced model, iAsk Pro, has set new records in accuracy for complex, graduate-level scientific problem-solving. This isn’t just a technical milestone — it’s a reimagining of how AI can understand, process, and answer challenging questions with human-like depth and precision. What is the GPQA Benchmark? What is the GPQA Benchmark? GPQA (Graduate-Level Google-Proof Q&A Benchmark) is one of the most rigorous tests for AI models, designed to challenge them in fields like biology, physics, and chemistry. These are not typical questions; they demand knowledge and nuanced, multi-step reasoning that can stump even PhD-level experts. Remarkably, iAsk Pro achieved a record-breaking 78.28% accuracy on the GPQA Diamond subset — comprising the benchmark’s most challenging 198 questions — outperforming leading models like OpenAI’s GPT and Anthropic’s Claude 3.5. This accomplishment sets a new standard in AI’s capacity to tackle the toughest, most intricate queries. iAsk Pro iAsk Pro Unlike general benchmarks, GPQA focuses on “Google-proof” questions that resist simple answers. These questions require advanced reasoning, the kind that rivals human experts. The complexity is so high that even specialized professionals typically average around 65% accuracy. iAsk Pro’s breakthrough accuracy reflects its unique ability to mirror the depth of human cognitive processing, setting it apart in the AI landscape. How iAsk AI Achieves Unmatched Accuracy How iAsk AI Achieves Unmatched Accuracy Unlike standard search engines that rely heavily on keyword matching, iAsk Pro’s approach goes far deeper. It uses Chain of Thought (CoT) reasoning to deconstruct intricate, multi-layered questions step by step. This method mirrors human logic, enabling iAsk Pro to deliver responses that are both highly accurate and contextually relevant. Users receive well-rounded, clear answers instead of vague references, underscoring iAsk Pro’s dedication to precision. Unlike standard search engines that rely heavily on keyword matching, iAsk Pro’s approach goes far deeper. It uses Chain of Thought (CoT) reasoning to deconstruct intricate, multi-layered questions step by step. This method mirrors human logic, enabling iAsk Pro to deliver responses that are both highly accurate and contextually relevant. Users receive well-rounded, clear answers instead of vague references, underscoring iAsk Pro’s dedication to precision. The GPQA benchmark was specifically designed to test AI models beyond surface-level knowledge, demanding advanced reasoning. iAsk’s choice to focus on this challenging benchmark was strategic, showcasing its capabilities in fields like academia, research, and other data-driven domains. With its high GPQA accuracy, iAsk Pro is poised to drive breakthroughs in areas that require deep scientific insight, establishing itself as an invaluable resource in advanced knowledge fields. The Future of AI-Driven Knowledge with iAsk Pro The Future of AI-Driven Knowledge with iAsk Pro For professionals, academics, and anyone who values precision, iAsk Pro heralds a new era of AI-powered inquiry. Its record-breaking performance points toward a future where technology not only aids information retrieval but actively advances collective understanding. From supporting scientific discoveries to offering users a reliable source of accurate knowledge, iAsk AI is reshaping the role of search technology in our lives. iAsk Pro’s success represents a step toward AI that can work alongside individuals as a problem-solver, capable of addressing the depth and complexity of human inquiry. This article is published under HackerNoon’s Business Blogging program. Learn more about the program here. This article is published under HackerNoon’s Business Blogging program. Learn more about the program here . here

iAsk AI Breaks Accuracy Records on AI’s Most Challenging Benchmark

iAsk KI breek akkuraatheidsrekords op KI se mees uitdagende maatstaf

iAsk AI ukax AI ukan juk’amp ch’amäki uka Benchmark ukanx Registros de Exactitud ukar p’akinti

iAsk AI bat rècords de precisió en el punt de referència més desafiant de l'IA

iAsk AI rompe récords de precisión en el parámetro de referencia más desafiante de la IA

iAsk AI kraze dosye presizyon sou referans ki pi difisil AI a

Az iAsk AI pontossági rekordokat döntött meg az AI legnagyobb kihívást jelentő referenciaértékén

iAsk AI、AIの最も難しいベンチマークで精度記録を更新

iAsk AI bije rekordy dokładności w najtrudniejszym teście porównawczym AI

iAsk AI thyen rekorde të saktësisë në pikën më sfiduese të AI

иАск АИ обара рекорде тачности на најизазовнијем мерилу АИ

iAsk AI AI-iň iň kyn görkezijisinde takyklyk ýazgylaryny döwýär

iAsk AI AIning eng qiyin mezonida aniqlik rekordlarini yangiladi

Artificial intelligence is steadily developing, becoming the center of discourse and a crucial debate topic among both experts and members of the general public. For some, the future of complete automation isn’t far away. In contrast, others are more reticent and call for more awareness regarding the possible issues the technology might bring, such as privacy violations, socioeconomic inequality, algorithmic biases and the use of deepfakes. While some discuss the possibility of AI in as few as five years or perhaps even less, other voices describe this scenario as pure science fiction, concluding that it will always be human beings dealing with the brunt of the decision-making. There’s no denying the potential AI can have to make the world a better, safer, more efficient place. For instance, firefighters have begun using AI-powered systems to better estimate the risk of wildfires, as well as respond to them promptly. reaching singularity As climate change remains a considerable issue, the figures estimate that artificial intelligence could reduce emissions by roughly 4%. But is AI itself sustainable now, or does it still have some way to go until earning this definition for itself? This is an area where the blockchain might be able to offer a helping hand. The blockchain Much like artificial intelligence, the blockchain operates by using large amounts of data. The system is shared among a network’s nodes and holds records and transactions that are fully decentralized. Generally, the blockchain is seen as a place , but research shows that it isn’t limited to cryptocurrencies. The main advantage of the blockchain is that it makes the information entered into the system completely immutable. That means that it cannot be altered or modified in any way, as well as deleted. where to buy Bitcoin Using the blockchain means improving security and ensuring all records’ safety and integrity, creating more transparent, trustworthy processes. The network is different from the classic databases or spreadsheets because of the way in which the data is stored. Within the decentralized ledger, the information is stored within blocks that are linked together through the use of cryptography. Scripts are used to enter and later access information. The blockchain is fully distributed, and there are many copies saved on different computers. For them to be valid, they must all match. The blocks are chained together through the hash. This hexadecimal number results from an encryption algorithm that goes through all the data in a completed block. The same hash then acts as the header of the following block, creating a cohesive chain, hence the name of the system. The issues Many popular AI tools like Chat GPT use graphics processing units to complete their calculations. Whenever someone asks the system a question, several devices in a data center amp up their processes to determine the answer. This naturally results in more considerable energy consumption, as well as the use of more water for cooldowns and to keep the computers running. The older models need roughly 500ml to come up with answers for every twenty questions. Since there’s a lot of interest in artificial intelligence at the moment, there’s also an overload of computational power. Cloud computing hosts websites and provides the necessary infrastructure for applications and highly efficient computing systems. The global cloud infrastructure already has a higher energy expenditure than the travel industry during pre-pandemic levels, as well as that of some countries. Recent studies have also shown that deep learning systems are also highly unsustainable and have large carbon footprints. Some can consume as much as five vehicles. This has drawn comparisons to the fossil fuel industry. Similar to data, oil is also a lucrative commodity once extracted and refined. Unfortunately, the other similarity is that both harm the environment. When considering all these aspects and when you realize that there’s a growing number of institutional and private AI users that will drive demands up, it becomes imperative for AI to become more sustainable to eliminate the risk of any potential damage. The improvements So, how exactly can the blockchain help abate these issues? One of the fundamental indicators lies in the mining process, particularly Bitcoin mining. Climate activists have targeted this relatively young and yet vibrant industry in the past due to its energy-intensive processes. Now, the market gets roughly 60% of its energy from renewable sources. The mining facilities that use either eco-friendly or untapped sources that can provide clean energy have begun accommodating their business strategies to meet the needs of artificial intelligence as well. If similar energy providers joined forces, the result would be the creation of a system that is not only environmentally friendly but can also support cost-saving schemes. Consumers are more likely to be attracted by a system that provides this functionality. Since the blockchain has a reputation for transparency, it could also be used to introduce a fully automated mechanism that ensures the allocation and management of carbon credits. This solution could act as an incentive, supporting the development of specialized infrastructure and propelling the adoption rates to meet the demand. There are currently networks that carry out their procedures on the blockchain, which are looking to speed the usage of cloud computing in both AI and Web3. Their expertise includes using scalable cloud resources and decentralized apps, completing the requirement for creating sustainable GPU tech. Scaling is another issue the blockchain can solve through workload distribution across several data centers. This system serves to reduce expenses as well by adopting creative and resourceful solutions such as using excess heat from data centers or using resources from idle machines. Developments in this sector could also find ways to mitigate other concerns, such as limited processing power. Unlike centralized systems, where data breaches result in massive shutdowns, the possibility of failure is minimized with the blockchain. The bottom line is one of the biggest technology trends in history, so the industry must ensure that it has an optimal head start. Solving any issues during its early days will pave the way for better adoption rates and guarantee success. The cornerstone is the integration of sustainable practices. If artificial intelligence is compatible with environmental protection, it can also become more feasible and deliver better results. Artificial intelligence

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