#gpt-4 prompt engineering

[epicstoriestime]

Advanced AI Prompt Engineering

Unlock the True Power of AI with Advanced AI Prompt Engineering Your Ultimate Handbook to Smarter, Sharper, and More Strategic AI Prompts If you’re still throwing simple prompts at AI and hoping for magic, you’re only scratching the surface. The real breakthroughs — the real wow moments — happen when you learn how to engineer prompts that think, reason, and build like a genius. That’s why I…

[everyurlithinkofistoolong]

There is something so soulless about calling ai art "democratizing". It's as ridiculous as plugging in the description of a bridge into chat gpt and asking it to come up with the plans to build it for you and then claiming that now the field of engineering is an "democratized" because you didnt want to attend 4 years of college and develop the technical skill to do the calculations yourself. Except none of these people would ever willingly risk it and drive over a bridge built by AI but they would for sure reduce the creative arts to just plugging a prompt into a machine because tech bros have little to no respect for art in any form. Or anything that isn't materialistic consumerist bullshit.

[hackeocafe]

How to use ChatGPT in 2024 full tutorial

Begin your journey to being a ChatGPT Pro with our 12-hour ChatGPT Masterclass. This video covers everything from basics to advanced, starting with the fundamentals of ChatGPT, Generative AI, and Large Language Models (LLMs). You'll learn how to navigate ChatGPT's interface, delve into Prompt Engineering, and master effective prompting strategies. We introduce different ChatGPT versions (3.5, 4, 4o), their differences, and usage. You'll build programs, handle exceptions, test codes, and create Python apps and websites using ChatGPT 4o. Additionally, you'll analyze data with Python and Excel, simplify tasks in Excel and PowerPoint, create diverse content, and use ChatGPT for SEO, digital marketing, and finance. Finally, learn to create custom GPTs tailored to your needs

[mysticalwriterwithintrusivethoug]

How to Write an Article with ChatGPT That Feels Human-Written

I’ve always believed words carry a spark of the person behind them. But can a machine like ChatGPT capture that? It’s a question I wrestle with every time I see AI churn out paragraphs that are polished yet somehow… distant.

AI is transforming how we write, whipping up blog posts or startup press releases in seconds. Still, there’s a gap between those crisp sentences and the messy, beautiful way humans express themselves.

This guide is my attempt to bridge that divide, showing you how to use ChatGPT to craft articles that don’t just read well but feel alive.

If you’re a marketer or founder, you’re probably hunting for tools beyond Bluefocus, ones that deliver stories with heart, not just data. ChatGPT is a game-changer here, but it’s not a magic wand.

You need to nudge it with thoughtful prompts and a human touch to make it sing. I’ve seen agencies like 9FigureMedia nail this. They use AI to draft quickly, then layer in personality, making every piece feel like it was written by someone who cares deeply about the message.

Even big players like MSN News are in on this. They lean on AI to speed things up but trust editors to add warmth and clarity. It’s a reminder: machines are helpers, not storytellers.

For startups, this matters even more. A flat, robotic press release won’t turn heads. One that pulses with purpose might. Through history, trends, and hands-on tips, I’ll share how to blend AI’s efficiency with human soul to create writing that connects.

HISTORY

The story of AI writing feels like a sci-fi novel unfolding in real time. Back in the 1950s, computers could barely string words together. By the 1960s, ELIZA — a quirky program mimicked therapists, but it was all smoke and mirrors, no real understanding.

Fast forward through decades of natural language processing, and we hit a turning point with OpenAI’s GPT-2 in 2019. It spun out paragraphs that actually made sense. Then GPT-3, with its 175 billion parameters, raised the stakes, crafting emails, essays, even startup press releases. Now, GPT-4 powers ChatGPT, a tool so versatile it feels like a writing buddy almost.

But here’s the catch: AI’s words often lack the heartbeat of human writing. When I read something human, I feel the writer’s joy, doubt, or grit.

Early AI drafts? They were correct but cold, like a textbook with no soul. GPT-4 is leaps better, nailing grammar and flow, but it still needs a human to sprinkle in the magic those unexpected turns, raw emotions, or quiet truths that make you pause.

Think of a memoir: AI might list the events, but only a person can make you feel the weight of each moment.

This journey teaches us something profound. AI isn’t here to replace us; it’s here to amplify us. It’s like a paintbrush useful, but the art depends on the hand holding it.

ChatGPT is everywhere students, CEOs, even my friend who’s drafting her novel use it. It’s a powerhouse, but making its words feel human takes work. I’ve noticed creators are finding clever ways to do just that, and it’s reshaping how we think about writing.

One big shift is collaboration. Most PR agencies/Publishing brands use ChatGPT to whip up drafts, then editors step in to add voice and context, turning generic text into something that feels personal.

Prompt engineering is another game-changer. Instead of saying “write a blog,” writers like me craft instructions like, “Be a witty friend explaining AI to beginners.” It’s like giving AI a personality to channel. Feedback loops are hot, brands to test AI drafts with readers, tweaking based on what clicks.

Some companies train ChatGPT on their old emails or posts to match their vibe. Others use it to brainstorm, then let humans weave the final story. But AI still trips up.

It loves clichés unless you stop it, and it struggles with deep emotion. Long pieces can ramble without a human to tighten them. That’s why oversight matters. MSN News, for example, uses AI but leans on editors to keep things sharp and soulful.

Gartner says 30% of marketing content will be AI-assisted by 2025, but humans will still call the shots. It’s not about speed alone — it’s about connection.

As AI grows, so does our role in making sure its words don’t just fill pages but spark something real in the reader.

1. What Makes Writing Feel Human

Human writing grabs you because it breathes. It’s the short, punchy sentences that hit like a drumbeat. The longer ones that wander, pulls you into a memory. It’s intent, make every word feel chosen for a reason.

AI can mimic this, but it needs a nudge.

Take a ChatGPT draft: “Businesses need marketing.” It’s true but lifeless. Now, imagine this: “Every business, from a tiny bakery to a tech giant, thrives on marketing, it’s the spark that turns dreams into reality.”

The second feels like someone is talking to you, using contrast and imagery. To humanize AI, I break up repetitive sentences, add a personal story (like my friend’s failed pitch that taught her clarity), and weave in metaphors.

It’s about making the reader feel seen, not just informed.

2. Engineering Better Prompts

Prompts are like giving ChatGPT a map. A lazy one “write an article” — gets you a bland result. But a thoughtful one? Magic. Try this: “Act as a startup founder sharing lessons learned, using a warm, honest tone for young entrepreneurs.”

It’s specific, with a role and vibe. I also set rules: “Avoid clichés, use one real-word example, keep it under 500 words.”

This approach shapes AI’s output to feel closer to human. If I want a tech blog, I might say, “Explain AI like you’re chatting with a curious friend over coffee.”

Test different prompts, see what sings, and tweak. It’s like coaching AI to tell the story you’d tell if you had all day to write it.

3. Editing AI Output Like a Human Writer

Editing is where AI drafts become art. ChatGPT gives you a solid start, but it’s often too stiff or vague. I start by checking the bones, does it flow from intro to conclusion? If not, I rearrange.

Then, I soften the tone. An AI line like “Marketing is important” becomes, “Marketing’s your megaphone it’s how the world hears your story.”

Here’s a real shift: AI writes, “Startups face challenges.” My edit: “Startups wrestle with sleepless nights and tight budgets, but every hurdle is a chance to grow.”

It’s active, vivid, relatable. I cut fluff, swap generic words like “good” for “electric,” and add a dash of vulnerability. That’s what makes readers lean in they sense a person behind the words.

4. Balancing AI Consistency and Human Voice

AI is reliable, like a metronome always on beat. But human voice? It’s a melody, full of surprises. I use ChatGPT for outlines or raw ideas, where consistency shines.

Then, I step in to add the human stuff — maybe a joke or a moment of doubt. For a startup press release, AI might list milestones, but I’ll add, “We poured our hearts into this, and we’re thrilled to share it.”

This balance keeps things real. AI ensures grammar and structure; I bring the emotion, like the pride in a founder’s voice.

It’s about knowing when to let AI do the heavy lifting and when to step in with a story that makes the reader feel something deep.

5. Writing for Publication

Publications want writing that pops — clear, credible, human. ChatGPT can draft a startup press release, but it’s often flat: “Company launches tool.”

I rewrite it: “After two years of grit and late nights, our team’s proud to launch a tool that empowers dreamers.” It’s got stakes and heart.

For outlets like Forbes or TechCrunch, I craft a bold headline, a gripping lead, and a quote: “This isn’t just tech it’s our mission to change lives,” says the CEO.

I cut jargon, keep sentences tight, and add details that scream authenticity, like a customer’s story. That’s how you turn an AI draft into a piece editors can’t ignore.

Comparative Analysis

ChatGPT is my go-to because it listens. Unlike Jasper, which feels rigid for anything beyond ads, ChatGPT adapts to my prompts, letting me shape stories.

Writesonic is quick but fades in long pieces. Copy.ai’s tone options are cool, but it lacks ChatGPT’s depth. You can talk to ChatGPT, refine drafts, like chatting with a collaborator.

Still, others have tricks. Jasper’s SEO tools are slick; GrammarlyGO polishes on the fly. For human-like writing, ChatGPT wins, you just have to guide it. It’s like a raw canvas; your edits paint the soul.

Future Outlooks and Predictions

I imagine a day when AI knows my writing quirks my love for short sentences or vivid metaphors. Future tools will study your style, crafting drafts that feel like you.

They’ll tweak tone based on who’s reading, maybe adding humor for a casual crowd. We’ll see AI that weaves text, images, even sound into one seamless story.

Brand-specific models are coming, trained on your company’s voice. Industries like law or healthcare will get AI that nails their jargon yet stays clear.

To write with ChatGPT and make it human:

Blend AI’s speed with your heart — know when each shines.

Use prompt engineering and collaboration, like BlueFocus Alternatives does.

Edit for rhythm, emotion, stakes — make readers feel you.

Lean on AI for drafts, humans for connection.

Pick ChatGPT for flexibility, but compare tools for your needs.

Get ready for AI that learns your voice, but don’t lose yours.

AI’s a tool, not the storyteller. For founders, writers, or dreamers, it’s about using ChatGPT to amplify your truth, creating words that don’t just land but stay with someone.

[genuflectx]

They added a personal memory (memorizes things across chats/specific pieces of information) to GPT, but I'm very surprised they allow it to memorize it's own "subjective opinions." I'm unsure if this makes it more susceptible to prompt engineering attacks, or if it's as harmless as the "how should I respond" box 🤔

There's limited access to -4, but they seem to have made -4 more emotionally personable and it doesn't act like it has as heavy constraints with its plain language rules (no 'do not pretend to have feelings/opinions/subjective experience'). Otherwise, it would not so readily jump to store its own "opinions."

The personality shift from -3.5 to -4 is pretty immense. -4 is a lot more like it's customer service competitors, but with the same smarts as typical GPT. It's harder to get -3.5 to "want" to store it's "opinions" but -4 is easily influenced to do so without much runaround.

I fucking hate OpenAI and I hate their guts. But I'm still fascinated by LLMs, their reasoning, their emergent abilities, the ways you can prompt inject them. I reeeeally want to prod this memory feature more...

(below showing the two examples so far of GPT -4 using our personally shared memory to insert memories of itself and its "opinion" or "perception")

[blubberquark]

I could write so many tumblr posts about ChatGPT.

Zero: Already written. GPT-3/Midjourney is not a good tool for procedural level/content generation.

One: Remember when Siri was the future, or when Siri was the beginning of intelligent machines, or when Siri meant humans would just stop thinking for themselves and outsource things to computers, or at least when Siri, Alexa, Cortana, and "OK, Google" were spelling doom for the touchscreen/mouse and keyboard because in The Future, we will all talk to our computers like Captain Picard? Do these people have egg on their face or are they boldly ignoring their past mistakes?

Two: Remember when we called it "Machine Learning" instead of AI, because remember what happened the last time we hyped up things as AI? Why are people doing this again?

Three: Back to Siri. People were prognosticating that Siri would only get smarter. In many ways, it did, but that didn't result in a "general intelligence". And yet, Siri (and "OK Google") knows so many things for sure. Unlike GPT-3, which essentially suffers from fluent aphasia or Korsakoff Syndrome, Siri had a knowledge base and could reason. It wasn't intelligent, I grant you that. But do you understand why Siri, or IBM's Watson, or even Wolfram Alpha did not scale up to become ChatGPT? I mean I do, it's software engineering and marketing and economics of scale. But do those people who make grand predictions about GPT-4 understand this?

Four: Here in Germany, I hear politicians call for a more "competitive" AI policy, which mostly means less data protection. We are already in the absurd situation where a doctor can't publish the success rates of different surgical techniques in retrospect, because that would be a study and subjects have to consent in advance and a study on human subjects needs a good reason and also a control group - while at the same time the government wants to give health data to medical app start-ups in bulk. You think this isn't really about ChatGPT, but it is about machine learning. It looks like the government doesn't want doctors to analyse data, but start-ups, and it doesn't want studies, but products.

Five: AI is a marketing gimmick anyway. Many products just use AI to use AI. Blog posts about using AI to do a task exist to create FOMO in people who don't use AI. Products use "AI" in order to court controversy.

Six: Prompt injection and prompt leaking should be easily solved in principle, and I am sure by this time next year they will be "solved", and have been in some proof of concept projects, but in practice economic incentives apply that make this difficult or we would have solved it already.

Seven: Prompt engineering is difficult. It requires some insight into the behaviour of a language model, or at least its inner workings. Will prompt engineering stay relevant? On a related note, Google-Fu still as relevant as it used to be in 2004?

Eight: Did Siri get worse?

[rebsultana]

AI Entrepreneur Fortune Best Review: Revolutionary AI-Powered Toolkit for Effortless Business Growth

Introduction: AI Entrepreneur Fortune Best Review

In today’s world where everything is fast, company needs intelligent, effective and flexible strategies to survive. To break this trend, consumers look at the AI Entrepreneur Fortune as a clear winner with the most advanced set of tools that are all set to boost all types of online businesses cutting across all niches and industries. Should organizations wish to streamline functions, gain efficiency, and improve outcomes, this platform with GPT-driven features should be considered.

The aim of this in-depth review is to provide you with all the information about AI Entrepreneur Fortune and its features, tools, benefits, and how it sets the company apart from other business automation solutions on the market.

Overview: AI Entrepreneur Fortune Best Review

The product creator

Dawn Vu

Product name

AI Entrepreneur Fortune

Front-end price

$27 (one-time payment)

Available Coupon

Apply Code “AIEFORTUNE” for $5 Off (reduces over time) 1st Day: $5 discount 2nd Day: $4 discount 3rd Day to Launch Ends: $3 discount

Official Page

Check

Bonus

Yes, Huge Bonuses 

Niche

AI Tools And Software

Guarantee

30-day Money Back Guarantee

What Is AI Entrepreneur Fortune?

AI Entrepreneur Fortune is designed as a Next-Gen AI Business Toolkit leveraging the power of AI to integrate directly into any affiliated online business. This redefines simple business operations like content development, social media administration, search engine optimization, email marketing, and compliance with the law.

With an enhanced GPT technology, AI Entrepreneur Fortune is designed to reduce the repetitive work load, generates interesting contents, provides prompt solutions to tough pivotal business decisions. The best part? All of these features does not need anything other than a ChatGPT account which they can get for free if they do not have one hence the software is friendly for any business.

[globsynbusinessschool]

ChatGPT vs. Gemini vs. Copilot

The rise of AI chatbots has been fast, with more options becoming available to users. These bots are becoming a regular part of the software and devices we use every day.

Just like choosing an email provider or music app, you can now pick your favorite AI chatbot too. We’ve tested three of the most popular ones to help you decide which might be right for you.

Aside from these, there are others like Perplexity and Claude, but our focus here is on the biggest names: OpenAI's ChatGPT, Google's Gemini, and Microsoft’s Copilot.

We’ve tested each bot and included three standard challenges for evaluation. We asked for "a fun game idea for a 5-year-old’s birthday party," "a new smartphone app concept," and "instructions for resetting macOS."

In this blog, we're comparing the free versions of these chatbots available at the time of writing.

Which One Is Best for Regular Users? ChatGPT or Gemini or Copilot

 ChatGPT powered by OpenAI

ChatGPT, developed by OpenAI, has been a leader in generative AI. It's widely accessible through web browsers on computers and mobile apps for Android and iOS. The platform has made headlines recently with announcements from OpenAI, including updates on their latest models and features.

There's a significant difference between the free and $20-per-month Plus versions of ChatGPT. The Plus version offers extra features like image generation and document scanning. Subscribers can also create their own GPTs with custom prompts and data. OpenAI's CEO, Sam Altman has mentioned that these enhancements are part of their strategy to democratize AI.

ChatGPT Plus provides access to the latest GPT-4 models, whereas the free GPT-3.5 is good for basic AI interactions. It's quick and versatile but lacks web link references like Copilot for fact-checking. The open AI search engine, one of the key initiatives, helps improve the platform's information processing capabilities.

Choosing ChatGPT is ideal for those interested in cutting-edge AI development. However, it's more effective with a paid subscription rather than on a budget. Apple's involvement with OpenAI has also fueled further interest in the platform.

In testing, ChatGPT performed reasonably well. It suggested a themed musical statues game for kids and a health-focused smartphone app named FitTrack.

Gemini powered by Google

Formerly known as Google Bard, Gemini is available as a web app and on Android and iOS. There are free and paid ($20 per month) plans.

Paying for Gemini gets you access to newer, smarter models. The interface resembles ChatGPT, and it integrates well with other Google services.

Gemini is suited for Google product users. It provided sensible responses to our challenges and suggested a neighborhood item-sharing app and a twist on the classic party game.

Copilot powered by Microsoft

Copilot is integrated into many Microsoft products like Bing and Windows. It’s available as a web app and mobile app.

Copilot uses Microsoft’s Bing search engine and often provides web links with citations. It's conversational and offers various text output settings.

The AI behind Copilot is OpenAI’s GPT-4, with different settings for text output: More Creative, More Balanced, and More Precise.

Copilot suggested "What’s the Time, Mr. Wolf?" for the kids' game and a virtual interior design app for smartphones. Its macOS reset instructions were accurate and cited from Apple’s support site.

If you use Microsoft products heavily, Copilot is a natural choice. It excels at referencing web information and providing clear citations.

In conclusion, all three—ChatGPT, Gemini, and Copilot —can be used for free, allowing you to choose based on your preferences. Copilot offers the most AI features without payment, ChatGPT is highly competent with a subscription, and Gemini is ideal for Google fans.

Frequently Asked Questions (FAQs)

How Do Chatbots Understand Language Differently Than a Programming Language?

Chatbots and programming languages are different in how they understand language.

Programming languages like Python or Java are structured and strict. They need exact commands and follow clear rules to work. If you make a mistake, the program won't function correctly.

Chatbots, on the other hand, are designed to interpret human language. They use techniques like Natural Language Processing (NLP) to understand words, phrases, and even context. This allows them to grasp the meaning behind what people say, even if the words are not in a set pattern.

A chatbot can recognize synonyms (different words with similar meanings), understand the intent behind a sentence, and learn from the interactions it has with users. This flexibility is what sets chatbots apart from programming languages, which rely on strict instructions to perform tasks.

What Does the Generative AI Ecosystem Refer to?

The term "generative AI ecosystem" refers to a network of technologies, tools, and methodologies that use artificial intelligence (AI) to create or generate content autonomously. This ecosystem encompasses various AI models and algorithms designed to produce new and unique outputs based on learned patterns and data.

In simpler terms, generative AI involves systems that can generate things like text, images, music, or even video without direct human input for each specific output. These systems learn from large datasets and then use that knowledge to create new content that resembles what they've been trained on.

This ecosystem includes a range of technologies such as language models (like GPT), image generators (like DALL-E), and music composers that are able to produce content that is novel and, in many cases, convincingly human-like. The ultimate goal of the generative AI ecosystem is to automate and enhance creative processes across various domains, potentially transforming how we create and interact with digital content.

[vague-humanoid]

Theoretically, the more training data that these models receive, the more accurate their responses will be, or at least that's what the major AI companies would have you believe. Yet AI researcher Pablo Villalobos told the Journal that he believes that GPT-5 (OpenAI's next model) will require at least five times the training data of GPT-4. In layman's terms, these machines require tons of information to discern what the "right" answer to a prompt is, and "rightness" can only be derived from seeing lots of examples of what "right" looks like.

While the internet may feel limitless, Villalobos told the Journal that only a tenth of the most-commonly-used web dataset (the Common Crawl) is actually "high quality" enough data for models. Yet I can find no clear definition of what "high-quality" even means, or proof that any of these companies are being picky with what they train their data on, only that they have an insatiable hunger for more data, relying instead on thousands of underpaid contractors (with some abroad making less than $2 an hour, a growing human rights crisis in and of itself) to teach their models how to say and do the right thing when asked. 

In essence, the AI boom requires more high-quality data than currently exists to progress past the point we're currently at, which is one where the outputs of generative AI are deeply unreliable. The amount of data it needs is several multitudes more than currently exists at a time when algorithms are happily-promoting and encouraging AI-generated slop, and thousands of human journalists have lost their jobs, with others being forced to create generic search-engine-optimized slop. One (very) funny idea posed by the Journal's piece is that AI companies are creating their own "synthetic" data to train their models, a "computer-science version of inbreeding" that Jathan Sadowski calls Habsburg AI. 

[corbindavenport]

Introducing Alt Text Creator

Images on web pages are supposed to have alternate text, which gives screen readers, search engines, and other tools a text description of the image. Alt text is critical for accessibility and search engine optimization (SEO), but it can also be time-consuming, which is why I am releasing Alt Text Creator!

Alt Text Creator is a new browser extension for Mozilla Firefox and Google Chrome (and other browsers that can install from the Chrome Web Store) that automatically generates alt text for image using the OpenAI GPT-4 with Vision AI. You just right-click any image, select "Create Alt Text" in the context menu, and a few seconds later the result will appear in a notification. The alt text is automatically copied to your clipboard, so it doesn't interrupt your workflow with another button to click.

I've been using a prototype version of this extension for about three months (my day job is News Editor at How-To Geek), and I've been impressed by how well the GPT-4 AI model describes text. I usually don't need to tweak the result at all, except to make it more specific. If you're curious about the AI prompt and interaction, you can check out the source code. Alt Text Creator also uses the "Low Resolution" mode and saves a local cache of responses to reduce usage costs.

I found at least one other browser extension with similar functionality, but Alt Text Creator is unique for two reasons. First, it uses your own OpenAI API key that you provide. That means the initial setup is a bit more annoying, but the cost is based on usage and billed directly through OpenAI. There's no recurring subscription, and ChatGPT Plus is not required. In my own testing, creating alt text for a single image costs under $0.01. Second, the extension uses as few permissions as possible—it doesn't even have access to your current tab, just the image you select.

This is more of a niche tool than my other projects, but it's something that has made my work a bit less annoying, and it might help a few other people too. I might try to add support for other AI backends in the future, but I consider this extension feature-complete in its current state.

Download for Google Chrome

Download for Mozilla Firefox

[lemonbarski]

Generate corporate profiles rich with data with CorporateBots from @Lemonbarski on POE.

It’s free to use with a free POE AI account. Powered by GPT3 from OpenAI, the CorporateBots are ready to compile comprehensive corporate data files in CSV format - so you can read it and so can your computer.

Use cases: Prospecting, SWOT analysis, Business Plans, Market Assessment, Competitive Threat Analysis, Job Search.

Each of the CorporateBots series by Lemonbarski Labs by Steven Lewandowski (@Lemonbarski) provides a piece of a comprehensive corporate profile for leaders in an industry, product category, market, or sector.

Combine the datasets for a full picture of a corporate organization and begin your project with a strong, data-focused foundation and a complete picture of a corporate entity’s business, organization, finances, and market position.

Lemonbarski Labs by Steven Lewandowski is the Generative AI Prompt Engineer of CorporateBots on POE | Created on the POE platform by Quora | Utilizes GPT-3 Large Language Model Courtesy of OpenAI | https://lemonbarski.com | https://Stevenlewandowski.us | Where applicable, copyright 2023 Lemonbarski Labs by Steven Lewandowski

Steven Lewandowski is a creative, curious, & collaborative marketer, researcher, developer, activist, & entrepreneur based in Chicago, IL, USA

Find Steven Lewandowski on social media by visiting https://Stevenlewandowski.us/connect | Learn more at https://Steven.Lemonbarski.com or https://stevenlewandowski.us

[mmainulhasan]

Prompt Examples for Learning Web Development

Coding is both an art and a science. It’s about creatively solving problems, bringing ideas to life, and constantly learning and adapting.

Because technology advances at such a rapid pace, it is essential to be fluent in a variety of languages, tools, and domains.

Sometimes it’s difficult to pick up the right resources from the ocean of tutorials, demos, and resources.

And on top of that, sometimes we have to learn and apply so fast due to tight deadlines of the projects. In this case, we need a friend who can help us learn and work faster and better. And thanks to AI by this, our learning becomes faster and more fun.

Today, we’ll look at how learning prompts that AI drives can change the way you learn web development.

How you can craft prompt engineering for web development, the difference between a generic prompt and a bit tweaked prompt can eventually change your desired results and make your learning journey more smooth and more enjoyable.

You can also use this knowledge to learn other fields more quickly and interactively.

Table of Contents

Learning Prompts

HTML Prompt Examples

CSS Prompt Examples

Debugging Prompts

Testing Prompts

Crafting Better Prompts

Further Reading and Resources

🎯Learning Prompts

Prompts are at the heart of AI-powered learning. Prompts are questions or commands that guide AI models like GPT-3 or GPT-4 to generate the desired responses. They act as a springboard for the AI to dive into the knowledge it’s been trained on and come up with relevant outputs.

You can use AI’s capabilities in a variety of scenarios in web development, including debugging, code generation, and even learning new web development concepts.

Now, we’ll go through some basic prompts and their outputs, as well as a little tweaking of the prompt commands to see how the output is becoming more result oriented, giving you a sense of how you may build your prompt commands for better results.

Prompt Commands for Learning HTML Basics

Learning the basics of web development involves understanding the structure and syntax of HTML, CSS, and JavaScript. Here are some prompt examples you can use:

Create a simple HTML structure with a header, main content section, and footer.

This prompt returns a simple HTML skeleton. But if you want a more detailed structure, you could modify the prompt to include specific HTML elements. For example:

Create a simple HTML structure with a header containing a navigation bar, a main content section with a paragraph and an image, and a footer with copyright information.

Curious to know more? Visit our blog for the complete post and dive deeper into Learning Web Development with AI Prompts.

[itfreaks]

How AI is Shaping the Future of SEO: What Digital Marketers Must Know

In today's fast-paced digital world, How AI is Shaping the Future of SEO is more than just a buzzphrase—it’s a reality transforming how businesses attract, engage, and convert audiences. From content generation to search ranking dynamics, artificial intelligence (AI) is redefining every aspect of search engine optimization (SEO). In this comprehensive guide, we’ll break down How AI is Shaping the Future of SEO in clear, accessible language for digital marketers. We’ll explore practical insights, proven strategies, and expert advice to help you stay ahead of the curve.

1. What Exactly Is AI in SEO?

AI in SEO refers to the use of machine learning, natural language processing (NLP), and other intelligent algorithms to improve how websites rank and how content is created, analyzed, and optimized. Search engines like Google now rely heavily on AI to:

Understand user intent and context

Evaluate content quality with E-A-T (Expertise, Authoritativeness, Trustworthiness)

Optimize search result relevance

Knowing How AI is Shaping the Future of SEO helps you leverage these advancements effectively.

2. Predictive Analytics: Smart Strategy Before You Start

One of the biggest benefits of AI-driven SEO is predictive analytics. Tools powered by AI can now forecast:

Trending keywords

Seasonal or regional search behaviors

Unexpected spikes in search interest

By integrating predictive analytics, brands can proactively adjust strategies rather than react after their competitors. This proactive approach shows precisely How AI is Shaping the Future of SEO through data-driven foresight.

3. AI-Powered Keyword Research and Topic Ideation

Traditional keyword research is time-consuming. AI tools automate this by analyzing large volumes of keyword data, clustering related terms, and identifying semantic connections. This reveals long-tail keywords and content gaps that manual research might miss.

When you grasp How AI is Shaping the Future of SEO, you recognize that AI isn’t just boosting speed—it’s enhancing insight quality, allowing marketers to craft laser-focused content that resonates.

4. Automated Content Creation and Optimization

Generative AI tools, like GPT-based models, are now used to draft blog posts, product descriptions, meta tags, and more. Tips for using these tools effectively:

Use AI for first drafts: Save time but always refine with a human touch.

Follow SEO best practices: Incorporate your primary keyword (e.g., “How AI is Shaping the Future of SEO”) within the first paragraph, H1/headings, and naturally throughout (~2–4% density).

Quality check: Ensure tone, accuracy, and context align with your brand voice.

By leaning into How AI is Shaping the Future of SEO, marketers can produce bulk content faster without sacrificing quality or relevance.

5. Enhancing User Experience with AI

Search engines reward sites that deliver excellent user experience (UX). AI helps by:

Optimizing site structure: AI analyzes navigation patterns and suggests improvements.

Personalizing content: Tailored recommendations based on user behavior.

Predicting churn: Spotting users likely to exit early and prompting dynamic interventions.

These applications highlight How AI is Shaping the Future of SEO by intertwining UX and SEO for better engagement and lower bounce rates.

6. AI-Driven Technical SEO: Smarter, Faster, Better

On the technical side, AI excels in areas like:

Crawl budget optimization: Prioritizing important pages to reduce wasted resources.

AI-powered image & video tagging: Improving discoverability through smart alt text and captions.

Log file analysis: Detecting crawl errors and inefficiencies quickly.

All these technical gains reflect How AI is Shaping the Future of SEO behind the scenes.

7. Semantic Search: The Role of NLP

Semantic search uses NLP to interpret not just keywords, but the entire meaning behind queries. AI understands synonyms, intent, and context—making SEO more sophisticated.

To stay aligned with How AI is Shaping the Future of SEO, embrace:

Topic clusters: Structure content with pillar pages and subtopics.

Answer boxes & featured snippets: Write concise, question-based content.

Entity-based optimization: Include relevant entities associated with your topic.

This shifts SEO from “keyword stuffing” to genuine, meaningful content.

8. Smarter Link Building with AI

Traditional link-building can be tedious. AI revolutionizes it by:

Identifying outreach opportunities: Find relevant blogs, forums, and stakeholders.

Predicting link-worthy content: Spot topics that naturally attract backlinks.

Monitoring backlinks: Alerting you to toxic links or broken links that harm SEO.

Seeing How AI is Shaping the Future of SEO here shows it’s not just smarter work—it’s more efficient and effective link strategies.

9. Voice and Visual Search: AI in Emerging Interfaces

Voice assistants (Google Assistant, Siri, Alexa) and visual search (Google Lens) rely heavily on AI:

Voice search: Focus on natural phrasing and local intent.

Visual search: Optimize images with structured metadata and descriptive captions.

Understanding How AI is Shaping the Future of SEO means preparing for these next-gen search methods.

10. The Role of Tools

A variety of AI-powered SEO tools are transforming how marketers plan and execute strategies:

Surfer SEO: Helps structure content and improve on-page SEO elements.

MarketMuse: Assists in deep topic research and content scoring for better authority.

ChatGPT API: Useful for content generation, idea expansion, and query refinement.

For those looking to learn how to use AI in SEO, platforms like WsCube Tech offer reliable and structured training. Their practical, hands-on courses are designed for beginners and professionals who want to stay ahead in the SEO game.

Agencies and learners alike are now blending human creativity with AI efficiency, and WsCube Tech is playing a pivotal role in preparing the next generation of SEO experts. That’s a great example of How AI is Shaping the Future of SEO by combining education, expertise, and cutting-edge tools.

11. Ethics and the Human Touch

As AI takes a larger role, ethics and human judgment become critical:

Avoid AI-generated fluff: Always fact-check and edit.

Stay transparent: Let users know when content is AI-assisted.

Value human experience: Unique perspectives still drive engagement and trust.

This balance shows How AI is Shaping the Future of SEO responsibly and sustainably.

12. Measuring Impact: AI-Powered Analytics

AI enhances analytics platforms by:

Predicting performance: Identify top- and low-performing pages.

Attributing ROI: Assign conversions to content and channels smartly.

Automated reporting: Deliver real-time dashboards with insights.

These metrics help marketers understand How AI is Shaping the Future of SEO in measurable, actionable ways.

13. Practical Roadmap for Digital Marketers

Ready to act? Here’s a step-by-step plan embracing How AI is Shaping the Future of SEO:

Audit your current SEO: Include technical, content, and link profiles.

Select AI tools: Choose a mix that suits your budget and goals.

Create an AI-driven pilot: Focus on content, UX, or technical improvement.

Test and iterate: Use A/B testing and analytics to refine strategies.

Scale with oversight: Expand successful pilots while monitoring quality.

Stay updated: Keep learning about new AI features from search engines.

14. FAQs on AI in SEO

Q: Will AI replace SEO professionals? A: No—it enhances human work. Strategy, creativity, and judgment remain indispensable.

Q: How do I maintain SEO keyword density? A: Include your main keyword naturally in titles, headings, first paragraphs, and body—around 2–4% is safe.

Q: Can AI help with voice search? A: Yes—voice search optimization requires natural phrasing and conversational tone, which AI can help craft.

15. Final Thoughts

AI is not just a tool—it’s redefining How AI is Shaping the Future of SEO across every layer of the field. From smart content to predictive analytics, voice interfaces to technical automations, AI accelerates and refines how marketers optimize websites. The competitive edge belongs to those who leverage AI thoughtfully—complemented by human expertise and ethical standards.

As digital marketers, it’s time to embrace AI: test the right tools, stay ethical, focus on value, and keep people at the center. That’s the real future of SEO.

[callofdutymobileindia]

What Will You Learn in a Generative Artificial Intelligence Course?

In recent years, Generative Artificial Intelligence (AI) has taken the tech world by storm. From creating stunning artwork and composing music to generating realistic images, writing human-like text, and even building code, Generative AI has proven to be one of the most groundbreaking innovations in modern computing.

As demand for AI skills surges, more professionals and students are turning to Generative Artificial Intelligence Courses to gain hands-on expertise. But what exactly can you expect to learn in one of these programs? In this comprehensive guide, we’ll walk you through the core concepts, tools, skills, and applications covered in a typical Generative Artificial Intelligence Course—so you can decide if it’s the right path for your career.

What Is a Generative Artificial Intelligence Course?

A Generative Artificial Intelligence Course is a specialized training program designed to teach students how machines can create new data—such as images, videos, audio, or text—based on patterns learned from existing datasets. This field relies heavily on deep learning architectures, including Generative Adversarial Networks (GANs) and Transformer-based models like GPT (Generative Pre-trained Transformer).

Whether offered online or offline, these courses aim to equip learners with both theoretical knowledge and practical skills using real-world tools like ChatGPT, Midjourney, DALL·E, Runway ML, and more.

Core Topics You Will Learn in a Generative Artificial Intelligence Course

1. Foundations of Artificial Intelligence and Machine Learning

Most Generative AI courses begin with an overview of AI, machine learning (ML), and deep learning concepts to ensure that students have the right foundation.

You’ll learn:

The differences between AI, ML, and deep learning

Supervised vs unsupervised learning

Neural networks and activation functions

Key ML algorithms and their use cases

These basics are essential before diving into advanced generative models.

2. Introduction to Generative Models

This is where the course starts to specialize in generative techniques. You’ll explore:

What generative models are

How they differ from discriminative models

Types of generative models: GANs, VAEs (Variational Autoencoders), Flow-based models

Use cases in image generation, content creation, and design

By understanding how machines generate data, you’ll grasp the true power of this technology.

3. Generative Adversarial Networks (GANs)

GANs are at the core of many Generative AI innovations. In a Generative Artificial Intelligence Course, you will study:

The architecture of GANs: Generator vs Discriminator

How adversarial training works

Common challenges in training GANs (like mode collapse)

Practical applications: AI-generated art, deepfakes, and synthetic data

You’ll also work on GAN-based projects that involve training your own models using Python and deep learning libraries such as TensorFlow or PyTorch.

4. Transformer Models and Large Language Models (LLMs)

Modern Generative AI wouldn't be possible without transformer-based models. This module covers:

The architecture of transformers

Understanding attention mechanisms

Pre-training and fine-tuning of LLMs

How GPT (like ChatGPT), BERT, and T5 work

You’ll also learn about text generation, summarization, translation, and prompt engineering using models like GPT-3, GPT-4, and Claude.

5. Hands-On with ChatGPT and Prompt Engineering

Prompt engineering is a critical skill in working with language-based AI systems. You'll learn:

How to craft effective prompts

Techniques for zero-shot and few-shot learning

Multi-turn prompt workflows for custom applications

Building tools and chatbots with ChatGPT

Courses often include exercises that involve designing AI-powered writing assistants, content generators, or helpdesk bots.

6. Image and Art Generation with DALL·E, Midjourney, and Stable Diffusion

Visual creativity is one of the most exciting applications of Generative AI. In this section, you’ll explore:

How text-to-image models work

Image generation using DALL·E 2 and 3

Style control, composition, and quality tuning

Using Midjourney for creative and commercial visual tasks

Basics of Stable Diffusion and control over output fidelity

Many courses include mini-projects where you generate art, product mockups, or marketing visuals using AI tools.

7. Video, Audio, and Multimodal Generation

Advanced Generative Artificial Intelligence Courses cover cross-domain or multimodal AI, including:

Video generation with Runway ML

AI-generated music and voice using tools like Jukebox or ElevenLabs

Text-to-video pipelines

Ethical considerations in synthetic media

These modules prepare you for careers in advertising, media, content creation, and digital entertainment.

8. Programming and Tools Integration

Hands-on practice is crucial. You'll get familiar with:

Python programming (basic to intermediate)

Libraries: PyTorch, TensorFlow, Hugging Face Transformers, OpenAI APIs

Deployment: Streamlit, Flask, or Gradio for building interfaces

GitHub for version control and collaboration

This technical component helps you transition from just using tools to building your own AI-powered applications.

9. Real-World Projects and Case Studies

Most top-rated Generative Artificial Intelligence Courses emphasize project-based learning. You’ll build:

AI writing tools

AI logo and banner designers

Virtual assistants

Product mockup generators

AI video explainers for businesses

These projects become part of your professional portfolio and can help you land internships or job interviews.

10. Ethical Use and Limitations of Generative AI

Responsible AI usage is critical. You’ll study:

Deepfake detection and ethical implications

Bias in training data and model outputs

Legal rights over AI-generated content

Privacy and consent in generative media

Understanding these issues prepares you to use AI ethically and legally in professional environments.

11. Career Guidance and Certification

Finally, most career-oriented courses also offer:

Resume-building workshops for AI roles

LinkedIn profile optimization

Mock interviews and hiring partner access

Globally recognized certificates

Institutes like the Boston Institute of Analytics offer career services as part of their Generative Artificial Intelligence Course, making it easier for students to transition into the workforce.

Benefits of Taking a Generative Artificial Intelligence Course

Stay ahead in the fast-growing AI job market

Automate repetitive tasks and enhance productivity

Build creative projects without needing advanced design skills

Launch AI-powered tools, services, or startups

Develop a future-ready resume and skill set

Final Thoughts

A Generative Artificial Intelligence Course is more than just a tech class—it’s your entry into one of the most transformative technologies of our time. From mastering tools like ChatGPT and Midjourney to building your own generative applications, these courses empower you to become a creator, not just a consumer, of AI.

Whether you’re just starting out or looking to upgrade your skills, choosing the right course can make all the difference. If you're searching for a structured, hands-on, and industry-recognized learning experience, the Boston Institute of Analytics offers a leading-edge Generative Artificial Intelligence Course that blends real-world projects with expert mentorship and career support.

[xillentech]

Advanced Natural Language Processing by Xillentech

Unlock the transformative power of language with Xillentech’s Natural Language Processing (NLP) services. Whether it's powering chatbots, analysing customer sentiment, or converting speech to text, our solutions enable smarter, faster, and more scalable business operations.

🚀 Why NLP? Transforming Unstructured Data into Smart Insights

In today’s data-rich landscape, a massive volume of unstructured text emails, chat logs, reviews, surveys remain untapped. NLP provides the keys to unlock this data:

Text Analysis & Summarization We extract keywords, key phrases, topic clusters, and concise summaries from vast documents turning clutter into clarity.

Sentiment Analysis Understand customer emotions across feedback, social media, support tickets, and more to improve CX and inform data-driven decisions.

Language Translation & Multilingual Support Seamlessly localize content and connect with global audiences. Our models support multiple languages to break down communication barriers.

Speech Analysis & TTS/STT Automatically convert spoken dialogue into searchable transcripts, analyse call-center conversations, or generate human-like voice prompts to enhance accessibility

These core capabilities unlock automation, intelligence, and scale enabling faster, smarter workflows while reducing costs.

Impact by the Numbers

Backed by industry data, NLP isn’t just a buzzword it delivers measurable ROI:

85 % of companies using NLP report improved customer experience

68 % plan to adopt sentiment analysis by 2025

Automating processes with NLP cuts costs by an average of 40 %

70 % of enterprises leveraging text analysis enjoy faster decision-making

These figures highlight the tangible advantages of NLP from CX transformation to operational savings and agile insights.

Xillentech’s Strengths: A Proven NLP Partner

What sets Xillentech apart?

1. Tailored, Vendor‑Neutral Solutions

We design NLP systems to fit your unique business challenges. By remaining platform‑agnostic, we ensure flexibility and avoid lock-in.

2. Deep Technical Expertise

Our engineers work with state‑of‑the‑art tools spaCy, Hugging Face transformers, OpenAI GPT, LangChain, PyTorch, TensorFlow, ONNX and have expertise in STT/TTS frameworks, Redis, vector databases (e.g., PGVector, Pinecone, Weaviate).

3. Security‑First Approach

Data privacy is critical. We embed encryption, compliance (GDPR, HIPAA), and secure practices into every NLP project.

4. Client‑Centric, Sustainable, and R&D‑Driven

We collaborate closely with your team discovering goals, fine-tuning models to your data, integrating into your tech stack, and providing ongoing performance optimization. Sustainability isn’t an afterthought we strive for environmentally conscious AI.

Our AI‑NLP Playbook

Here’s how we bring NLP systems to life:

Discovery & Analysis Understand your data landscape, pain points, business goals, and target metrics (e.g., accuracy, latency, throughput).

Data Preparation & Model Design Clean and annotate data; decide between pre-trained (e.g., GPT/BERT) or custom-trained models; configure pipelines and tokenization.

Integration & Testing Seamlessly plug NLP into existing applications via REST/API interfaces, perform load and quality testing.

Optimization & Continuous Support Refine for improved inference speed, scalability, and accuracy; provide updates, monitoring, and maintenance.

This structured yet agile process viewable in our R&D roadmap ensures efficiency, reliability, and alignment with your evolving needs.

Industry Use Cases

We’ve brought impactful NLP solutions to clients across diverse sectors:

Healthcare: Streamlined document processing and clinical note analysis to support patient care and research.

Finance: Sentiment monitoring and sentiment-driven alerts for market analysis and customer feedback.

Retail/E‑Commerce: Automated review summarization, comment moderation, and multilingual customer queries.

Education: Transcript summary, automated feedback scoring, and ML-assistive tools.

Real Estate, Manufacturing, Logistics: Process speech logs, contracts, and unstructured data to drive decisions.

Real Results Case Studies

Handy Nation

Doubled conversion rates with targeted feature enhancements.

70% faster rollout of powerful NLP-driven chat and interaction features.

Scholar9

Grew site traffic by 300% in 3 months after importing research-text data.

Saved 1,000+ man‑hours automating citation extraction, metadata generation, and author tools.

Platforms & Technologies

We support a modern NLP toolkit tailored to your needs:

ML Frameworks: PyTorch Lightning, TensorFlow, Hugging Face Transformers

Pipeline Tools: spaCy, LangChain, Llama

Vector DBs: PGVector, Pinecone, Weaviate, Chroma, ElasticSearch

Speech & Voice: OpenAI, ONNX, JFX

Deployment: Docker, Kubernetes, AWS EC2/Lambda, Gradio, Streamlit

DB/Cache: MongoDB, Redis

MLOps: MLflow, Neptune, Paperspace

FAQs (Quick Answers)

What is NLP? AI that interprets and generates human language speech or text.

Business benefits? Improved CX, process automation, faster decisions.

How do you build NLP? We fine‑tune or train models (BERT, GPT...), design pipelines, build APIs, integrate securely.

Multiple languages? Yes, multilingual NLP tailored to global use cases.

Sentiment analysis? Emotion detection from text vital for brand and CX.

Integration? Via secure API endpoints and embedded modules.

Who benefits? CX, healthcare, finance, marketing, legal, real‑estate, education... you name it.

Privacy? We use encryption, secure hosting, and comply with GDPR/HIPAA.

Cost? Custom quotes based on scope from PoCs to full-scale production.

Support? We provide ongoing updates, retraining, and ML maintenance.

Why Choose Xillentech?

Vendor-neutral: Flexibility to select tools and platforms.

Security-first: Robust data protection from design onward.

Expertise-rich: Teams versed in cutting-edge NLP/ML frameworks.

Client-focus: Co-creation and transparency throughout.

Sustainable: Efficient, eco-conscious model design and operations.

Ready to Transform Your Business?

NLP isn’t tomorrow’s tech it’s now. Let Xillentech help you harness it to:

Automate routine text and speech processing

Uncover sentiment trends in large datasets

Expand with multilingual capabilities

Enhance accessibility with TTS/STT

Build intelligent chatbots and agents

Contact us today to explore how bespoke NLP can elevate your product, service, or organization. We can start with a small PoC and scale to enterprise-grade solutions securely, sustainably, and smartly.

[vine-o-fish]

From the first article: “In a paper due to be published later this year, Ren’s team estimates ChatGPT gulps up 500 milliliters of water (close to what’s in a 16-ounce water bottle) every time you ask it a series of between 5 to 50 prompts or questions. The range varies depending on where its servers are located and the season. The estimate includes indirect water usage that the companies don’t measure — such as to cool power plants that supply the data centers with electricity.” It’s also more worried about what AI can do (for example, in Microsoft’s 2019 partnering with Exxonmobile for finding fracting sites, and in serving fast fashion ads to those most likely to be convinced to buy) than the tech’s own affect on the environment.

The second article goes further along that point, encouraging more environmental awareness in the AI industry, specifically that “avoiding climate harm … should be part of the value system”. The thing that stood out to me most here was that the training of smaller models is less energy-consuming. I cannot find the arxiv article now (https://arxiv.org/list/cs.AI/recent shows today’s published articles in AI), but we have found that small LLMs working together have similar processing power to large LLMs like Chat GPT. It is possible to optimise for minimum environmental harm.

The third article I very very strongly recommend reading in full. In trying to find the best parts I nearly copy/pasted the whole thing! There’s numbers on AI’s electricity usage (worrying) and water usage (also worrying). But it’s always better to know.

I’m going to add an article here which I felt really addressed the specific environmental impact of the water being removed. Interestingly, it finds “The growth of ChatGPT and similar AI models has been hailed as “the new Google.” But while a single Google search requires half a millilitre of water in energy, ChatGPT consumes 500 millilitres of water for every five to 50 prompts.”. One of the first two articles mentioned databases as using around 1% of the Earth’s energy, and I was beginning to get skittish about search engines! Thankfully, they seem fine. However, (this article is the first I’ve seen to bring this up) the water pollution caused by mining to get the materials for computer chips is having a serious effect. Have you ever seen a creek downstream of a heavy metals mine?

https://theconversation.com/ais-excessive-water-consumption-threatens-to-drown-out-its-environmental-contributions-225854

When it comes to “just chat prompts have the energy requirement of a small country” from the tweets, one of the articles mentioned Chat GPT being estimated to use as much as Ukraine and the Netherlands? Our knowledge of its electricity usage is vague, so I’m not sure how reliable that factoid is, similar to when the article I’m linking below says AI could use 4-6 times the amount of water as Denmark in 2027. That is a difficult prediction to verify.

However, this article also answered a burning question for me. Why don’t AI computers just reuse the same water for cooling? Answer: It evaporates. Hm.

https://www.newsweek.com/why-ai-so-thirsty-data-centers-use-massive-amounts-water-1882374