There was a time when choosing an AI assistant was easy.

You opened ChatGPT.

Done.

Today?

ChatGPT, Claude, Gemini, Grok… and then another 47 tools appear before you've even finished your coffee. 😁

The interesting part is that there isn't necessarily one AI that's best at everything.

Some are excellent general-purpose assistants.

Some are better at deep reasoning.

Others are great with images, documents and data.

And some are particularly useful when you need fast answers or creative brainstorming.

The original vc.ru article compares seven AI models and also highlights several principles for writing better prompts.

So instead of asking:

“Which AI is the best?”

Let's ask a much more useful question:

“Which AI is best for this particular job?”

🥇 1. ChatGPT — The All-Rounder

Let's start with the obvious one.

ChatGPT is the tool you can throw almost anything at.

Need to:

• write something;
• brainstorm ideas;
• explain a difficult topic;
• analyze a document;
• write code;
• create a plan;
• generate content?

It's usually a solid choice.

Its biggest advantage is versatility.

Think of it as a Swiss Army knife.

It may not be the absolute best specialist for every single task, but it can handle a ridiculous number of different jobs.

🥈 2. Claude — When You Need More Depth

Claude becomes especially interesting when you're dealing with:

• long documents;
• complex reasoning;
• detailed analysis;
• editing;
• research;
• long-form writing;
• code.

For example:

“Here's a 40-page document. Identify contradictions, summarize the major arguments and propose a new structure.”

That's where a deeper reasoning model can really shine.

Claude feels less like:

“Give me something quickly.”

and more like:

“Sit down. Let's actually figure this out.”

😄

🥉 3. Gemini — When Text Isn't Enough

Gemini's major advantage is its ability to work across different types of information.

You can give it:

📷 images
📊 spreadsheets
📄 documents
💻 code

and ask questions about them.

For example:

“Here's a screenshot of my advertising dashboard. Find unusual numbers and explain what might be happening.”

Or:

“Analyze this sales spreadsheet and identify products whose performance has suddenly dropped.”

That's a very different way of using AI.

⚡ 4. Grok — For Ideas and Brainstorming

Grok is often interesting when you want a more direct conversational style and unconventional ideas.

It's useful for:

• brainstorming;
• creative concepts;
• alternative perspectives;
• unusual ideas;
• exploratory conversations.

For example:

“Give me 20 provocative ideas for an advertising campaign. Avoid generic marketing clichés.”

That's where a more unconventional style can be useful.

But remember:

A confident answer isn't necessarily a correct answer.

Facts still need verification.

🚀 5. Claude Sonnet — The Everyday Workhorse

If the heavier Claude models are the big guns, Sonnet is more like your everyday workhorse.

Translate something?

Sure.

Summarize a document?

Sure.

Write a social post?

Yep.

Explain a concept?

Absolutely.

Generate headline ideas?

Easy.

For everyday work, you often don't need the most powerful model available.

Using a supercomputer to translate “Hello” is a little excessive. 😂

🧠 6. Advanced Models — For Complex Problems

Many AI platforms offer more powerful versions of their models.

Their purpose is simple:

More complex tasks can benefit from more reasoning power.

For example:

“Analyze three business models, compare their economics and risks, identify dependencies and create a testing strategy.”

That's very different from:

“Write a birthday message.”

Using the most powerful model available for every tiny task is like driving a Ferrari to buy a loaf of bread.

You can.

But… why? 😁

⚡ 7. Smaller Models — When Speed Matters

Not every question requires a 15-page philosophical essay.

Sometimes you just need:

“Translate this.”

Or:

“Fix this sentence.”

Or:

“Give me three headlines.”

For those tasks, smaller and faster models can be perfect.

The principle is simple:

Don't use a hammer to press a button.

🏆 So Which AI Is Actually the Best?

Here's the honest answer:

It depends on the job.

A practical starting point looks like this:

Task

AI to try

General-purpose work

ChatGPT

Deep analysis

Claude

Images + data

Gemini

Brainstorming

Grok

Everyday writing

Claude Sonnet

Complex reasoning

Advanced models

Quick routine tasks

Smaller models

This isn't really a “winner's table.”

It's more like a toolbox map.

And that's a much more useful way to think about AI.

🤯 But There's a Bigger Problem Than Choosing the AI

You can use the most powerful AI model available.

And still get…

complete nonsense.

Why?

Because your prompt was:

“Tell me about marketing.”

That's like walking into a professional consultancy and saying:

“So… tell me something.” 😂

🎯 How to Ask AI Better Questions

1. Give Context

Bad:

“Write a Telegram post.”

Better:

“Write a Telegram post about AI tools. The audience is beginners aged 25–45. Use simple language, a friendly tone and light humor. Keep it around 1,500 characters.”

Now the AI actually knows what you're trying to do.

👤 2. Give It a Role

For example:

“You are an experienced Telegram marketing strategist.”

This gives the model a useful frame for the response.

You don't need a three-page description of the role.

One clear sentence is often enough.

📐 3. Specify the Format

Want a table?

Say:

“Create a table.”

Want five ideas?

Say:

“Give me five options.”

Want something short?

Say:

“Keep each answer under 50 words.”

The clearer the format, the less time you'll spend saying:

“No, no, no… not like that.” 😁

🧩 4. Give It the Information It Needs

Don't ask:

“Create an ad for my product.”

Instead, provide:

• product description;
• benefits;
• target audience;
• price;
• customer objections.

Then say:

“Create an advertising campaign using this information.”

The difference can be huge.

🔄 5. Iterate

This is one of the most important habits.

The first answer isn't great?

Don't throw the conversation away.

Tell the AI:

“Make it less formal.”

Then:

“Add more concrete examples.”

Then:

“Remove generic advice.”

Then:

“Make it more conversational and slightly humorous.”

That's how you get closer to the result you actually want.

Good prompting isn't always one perfect prompt.

Sometimes it's simply a good conversation.

💡 One More Question You Should Always Try

After getting an answer, ask:

“What am I missing?”

Or:

“What are the weaknesses in your answer?”

Or:

“What alternative approaches haven't we considered?”

This turns AI from a text generator into something closer to a digital sparring partner.

And that's much more useful.

🚨 The Bottom Line

You don't need to find one AI that does everything.

Build your own toolkit.

For example:

ChatGPT → general tasks

Claude → long-form writing and analysis

Gemini → images and documents

Grok → brainstorming and unconventional ideas

Smaller models → routine work

Then switch depending on what you're actually doing.

Because:

“Which AI is the best?”

isn't always the right question.

A better question is:

“Which AI is best for my specific task?”

And that question can save you a surprising amount of time.

🎁 Want ready-to-use AI materials?

Subscribe to the channel and head over to the Telegram bot.

You can choose one bonus or grab both:

🔥 “Universal Prompt Templates for Marketers”

💰 “8 Neural Networks for Making Money Online”

👉 Get your bonuses

Grab both — they'll be useful for working with AI and exploring ways to monetize AI tools. 🚀

Writing a draft with ChatGPT is easy.

Making that draft clear, natural and actually enjoyable to read is another story.

You type:

“Make this better.”

ChatGPT happily does exactly that.

And suddenly your simple paragraph sounds like:

“In today's rapidly evolving digital landscape…”

Oh no. 😂

The problem isn't necessarily ChatGPT.

The problem is that “make it better” doesn't tell the AI what better actually means.

Should it be shorter?

More persuasive?

More casual?

More professional?

More human?

Less repetitive?

You need to tell it.

The most effective approach is to treat ChatGPT as an editor and give it one specific editing goal at a time. (DTF)

🧠 Start by Giving ChatGPT a Role

Before editing the actual text, give it a clear role:

Act as a professional editor.

Your job is to improve the readability,

clarity and coherence of the text

without changing its original meaning or facts.

Pay attention to:

— grammar;

— punctuation;

— sentence structure;

— repetition;

— unnecessary jargon;

— logical transitions;

— natural language.

This gives the model a clear editorial direction.

✍️ 1. Make the Text Easier to Read

For long sentences and complicated wording:

Rewrite this text to make it easier to read.

Shorten long sentences.

Remove unnecessary jargon and complicated constructions.

Keep the original meaning, facts and numbers.

Text:

[insert text]

Simple?

Yes.

Useful?

Very.

Especially when the original draft was written at 2 a.m. and your brain had already left the building. 😂

🎯 2. Adapt the Tone to Your Audience

A text written for developers shouldn't sound like a text written for beginners.

Tell ChatGPT who the reader is:

Rewrite this text for the following audience:

[describe audience].

Use clear and accessible language.

Avoid unnecessary technical terms.

Keep the content useful and credible.

This immediately gives the AI a much clearer target.

✂️ 3. Shorten the Text Without Losing the Meaning

Sometimes the text isn't bad.

It's just way too long.

Try:

Reduce this text by 50%.

Keep:

— the main idea;

— key facts;

— numbers;

— important arguments.

Remove repetition, secondary details and filler.

Do not add new information.

This is especially useful for social media posts, emails and platforms with character limits.

🤖 4. Remove the “AI Writing” Feel

One of the most useful editing tasks today.

Because sometimes AI-generated text is so polished that you immediately know it was written by AI.

Try:

Rewrite this text so it sounds natural and human.

Remove:

— common AI phrases;

— corporate clichés;

— unnecessary introductory phrases;

— artificial enthusiasm;

— generic statements;

— overly polished marketing language.

Use concrete, natural and conversational wording.

Do not change the facts or meaning.

The goal isn't to make the text “less professional”.

It's to make it sound like a real person actually wrote it.

💪 5. Make the Argument More Convincing

If the text needs to persuade rather than simply inform:

Rewrite this text to make the argument more convincing.

Remove weak and vague statements.

Replace rhetorical questions with direct statements.

Keep the original facts.

Do not invent unsupported information.

Text:

[insert text]

Useful for:

• sales pages;
• proposals;
• marketing content;
• presentations;
• business communication.

🔎 6. Just Fix the Errors

Sometimes you don't want a rewrite.

You simply want the mistakes fixed.

Use:

Proofread this text for:

— spelling;

— punctuation;

— grammar;

— word agreement.

Do not change the style or structure.

After the corrected version,

list the changes separately.

This distinction matters:

proofreading ≠ rewriting.

If you ask AI to “improve” something, it may change much more than you wanted.

💼 7. Make Business Emails Sound Human

Business emails can become painfully formal.

Sometimes you read a sentence and think:

“Was this written by a person or a government department?” 😂

Try:

Rewrite this business email.

Keep it professional,

but make it warmer and more human.

Remove unnecessary formality.

Keep the original meaning and request.

End with a polite call to action.

Text:

[insert email]

You can also ask for a softer, stricter or more neutral tone.

📱 8. Adapt Content for Telegram

One piece of content can often be reused across several platforms.

Instead of rewriting everything manually:

Adapt this text for Telegram.

Keep the main idea and facts.

Use:

— short paragraphs;

— clear subheadings;

— natural conversational language;

— easy-to-scan formatting;

— an engaging opening.

Do not turn the text into an advertisement.

Text:

[insert text]

The same basic idea can be adapted for Telegram, LinkedIn, email and other formats.

🔗 9. Fix Awkward Paragraph Transitions

Sometimes every paragraph looks fine individually.

But together?

It feels like:

idea → another idea → completely random idea.

😂

Try:

Analyze the transitions between paragraphs.

Find places where the ideas feel disconnected.

Suggest short transition sentences

that make the text flow more naturally.

Do not change the content of the paragraphs.

A tiny change can make a surprisingly big difference.

🧐 10. Ask ChatGPT to Explain Its Edits

This is useful if you want to learn from the editing process instead of simply receiving a polished version.

Rewrite and improve this text.

After the rewritten version, explain:

1. what mistakes you found;

2. which sentences you changed;

3. why you changed them;

4. which style problems you noticed.

After doing this regularly, you'll start noticing the same problems yourself before sending the text to AI.

🧩 The Most Important Rule: One Pass, One Goal

This is the key idea.

Don't say:

❌ “Make this text perfect.”

Say:

✅ “Reduce this text by 30%, remove repetition and preserve all facts.”

Then:

✅ “Make the tone more conversational.”

Then:

✅ “Adapt it for Telegram.”

Finally:

✅ “Proofread the final version for grammar and punctuation.”

In other words:

clarity → tone → shortening → final proofreading.

Short, focused passes give you much more control over the result.

⚠️ One More Important Thing

Don't completely outsource fact-checking to AI.

ChatGPT can improve a sentence beautifully while quietly changing its meaning.

Be especially careful with:

• numbers;
• dates;
• facts;
• quotations;
• legal wording;
• names;
• technical specifications.

AI is excellent at improving how something is written.

But you should still be responsible for what the text actually says.

🚀 The Simple Workflow

If you want the whole process in one line:

Write → clarify → improve → adjust tone → shorten → adapt → proofread.

That's much more reliable than asking:

“Make it better.”

You don't need one magical prompt.

You need a repeatable editing process.

And once you have that, ChatGPT becomes much more useful as an everyday writing assistant.

🎁 Want ready-to-use AI materials?

Subscribe to the channel and head over to the Telegram bot.

You can choose one bonus or grab both:

🔥 “Universal Prompt Templates for Marketers”

💰 “8 Neural Networks for Making Money Online”

👉 Get your bonuses

Grab both — they'll be useful both for working with AI and exploring ways to monetize AI tools. 🚀

Not long ago, using AI as a student could easily look like cheating.

Today, things are changing.

According to a VK Education survey, around 85% of students use AI assistants to improve their study productivity. The survey included more than 1,000 university students aged 18–25.

And this is no longer just an experiment for tech enthusiasts.

AI is becoming a normal part of student life — just like search engines, smartphone notes and online calendars.

But here's the interesting part.

🤖 Students don't have to do everything manually anymore

Imagine this:

You have an exam tomorrow.

The subject is complicated.

Your notes are 80 pages long.

And your brain contains approximately three useful pages — one of which is currently thinking about why you ever enrolled in university. 😁

In the past, there were basically two options:

read everything;

or

pretend you're reading everything.

Now there's another option.

You can ask AI to:

• explain difficult concepts;
• summarize complex material;
• create self-test questions;
• analyze your mistakes;
• build a study plan;
• generate practice exercises.

And this can be genuinely useful.

🧠 But there's a huge difference between learning WITH AI and learning THROUGH AI

This is probably the most important point.

You can simply ask:

“Write my 20-page research paper.”

Get the finished document.

Submit it.

And learn absolutely nothing.

Technically, the task is complete.

Educationally?

Not so much.

😂

A much better approach is to use AI as a personal tutor.

For example:

“Explain this topic as if I'm completely new to it. Then ask me five questions and don't show the answers until I try them myself.”

Now AI isn't simply producing the result.

It is making you interact with the material.

That's a completely different use case.

🎓 AI can become a personal tutor

A textbook explains information the same way to everyone.

A teacher can adapt the explanation — but there may be 30 students in the classroom.

AI can adapt the explanation to one person.

Didn't understand?

“Explain it more simply.”

Still confused?

“Give me a real-world example.”

Now you understand?

“Give me a harder problem.”

And the best part?

The AI doesn't say:

“I've already explained this three times.”

😂

⏱️ What about productivity?

The survey also highlights another problem: students want to try new approaches to time management and study organization, while only 2% say they are completely satisfied with their current approach.

So the problem isn't only knowledge.

It's also how the learning process is organized.

AI can help here too.

Instead of:

“I need to prepare for an exam.”

Ask:

“I have seven days and 12 topics. Create a realistic study schedule with revision and self-testing.”

Suddenly, the giant scary task becomes:

Day 1 → Topics 1–2

Day 2 → Topics 3–4

Day 3 → Revision + test

Much easier.

🧀 The “Swiss Cheese” method vs procrastination

The survey found that students are particularly interested in the “Swiss Cheese” method — breaking a large task into smaller steps. The “Eat the Frog” approach and good old last-minute deadline panic also made the list. 😁

The Swiss Cheese approach works extremely well with AI.

Instead of:

“Write my thesis.”

Break it down:

1. choose the topic;
2. define the main question;
3. create the structure;
4. build an outline;
5. collect materials;
6. write the first section;
7. check the arguments;
8. edit the text;
9. prepare the presentation.

Suddenly, the monster becomes a checklist.

📱 How do students organize their work?

The survey shows that students still rely heavily on very simple tools: smartphone notes and memory. Online calendars, planning apps and paper or digital planners are also used.

So you don't necessarily need an expensive productivity system.

Sometimes this is enough:

notes + calendar + AI + discipline.

That last part, unfortunately, is still difficult to automate. 😁

🧹 Even cleaning your desk can help

There's another interesting detail.

More than half of the surveyed students tidy their workspace to get into study mode. 45% take a short break and switch to entertainment content, while 40% listen to background music.

So the classic routine becomes:

clean desk → music → laptop → AI → study.

And then comes the dangerous moment:

“I'll just watch one productivity video first…”

Forty minutes later:

“Why am I watching a review of refrigerators?” 😂

🎯 What actually motivates students?

This may be the most interesting part of the survey.

76% of students said that understanding the connection between education and their future career is a key source of motivation.

That makes sense.

If studying feels like:

“Memorize these 40 pages because you have to.”

Motivation disappears quickly.

But if it becomes:

“I'll need this skill for my future career.”

The whole thing feels different.

AI can help make that connection clearer.

Ask:

“Which real-world jobs use these skills?”

Or:

“Give me five practical tasks I could solve with this knowledge.”

Suddenly, a boring academic topic becomes a useful tool.

⚠️ But there's a major trap

AI can do homework.

And that's exactly why it can make students less capable if used badly.

If you constantly outsource:

• essays;
• problem solving;
• analysis;
• conclusions;
• presentations;

you may eventually discover something unpleasant:

the work is finished, but the knowledge never arrived.

So change the way you ask.

Instead of:

“Solve this problem.”

Try:

“Give me a hint, but don't show the solution.”

Instead of:

“Write my essay.”

Try:

“Help me create the structure and point out weaknesses in my arguments.”

Instead of:

“Explain this topic.”

Try:

“Explain it and then test whether I actually understood it.”

That's a much smarter use of AI.

🚀 What could studying with AI look like in 2026?

Pretty simple.

Step 1

Choose a topic.

Step 2

Ask AI to explain it simply.

Step 3

Ask questions.

Step 4

Solve several problems yourself.

Step 5

Give your answers to AI.

Step 6

Ask it to identify mistakes without fixing everything for you.

Step 7

Review what you got wrong.

Step 8

Take a short test.

You now have something close to a personalized learning loop.

And you don't need to reread the same paragraph 17 times hoping enlightenment will eventually arrive.

🔥 The biggest change has already happened

AI is no longer just a text-generation tool.

It is becoming an interface between people and knowledge.

You can talk to it.

Ask questions.

Challenge it.

Ask for another explanation.

Test yourself.

Simulate an exam.

Analyze mistakes.

Build a study plan.

That's why the 85% figure is so interesting.

The question is no longer simply:

“Will students use AI?”

The more important question is:

Who will learn to use it properly — and who will simply press “Do it for me”?

In the first case, AI makes a person stronger.

In the second, it slowly replaces the skills that person was supposed to develop.

And that's the real educational revolution.

🎁 Want ready-to-use AI materials?

Subscribe to the channel and head over to the Telegram bot.

You can choose one bonus or grab both:

🔥 “Universal Prompt Templates for Marketers”

💰 “8 Neural Networks for Making Money Online”

👉 Get your bonuses

Grab both — they'll be useful for working with AI and exploring ways to monetize AI tools. 🚀

Remember when AI was mostly about asking questions and getting surprisingly confident answers?

Write an email.

Summarize an article.

Explain quantum physics as if you actually understood it. 😁

Then came NotebookLM.

Google took a different approach:

Instead of asking AI about everything, give it your own sources and let it work with them.

Upload PDFs, documents, websites and other materials, and NotebookLM can help you analyze them, answer questions, find connections and transform information into more useful formats. Google describes it as an AI-powered research assistant grounded in the sources you provide.

And the tool has changed dramatically.

In July 2026, Google officially renamed NotebookLM to Gemini Notebook, while keeping it as a standalone product that is becoming much more deeply connected to the Gemini ecosystem.

And that's where things get interesting.

🧠 Why Was NotebookLM So Useful in the First Place?

Imagine having 30 documents to study.

You could open every file.

Read everything.

Take notes.

Make a spreadsheet.

And then spend 20 minutes trying to remember where you saw that one important paragraph.

Or you could upload the material to NotebookLM.

The system can answer questions based on your sources and provide citations back to them.

The difference is huge.

Instead of:

“What do you know about marketing?”

you can ask:

“Here are 20 customer research reports. Find the problems that appear across all of them.”

That's a completely different workflow.

📚 From Project Tailwind to an AI Research Partner

The story started in 2023 with Google's experimental Project Tailwind.

The basic idea was simple:

give AI a collection of documents and let it work specifically with those materials.

The experiment survived and became NotebookLM.

Then things accelerated.

Google added more ways to work with sources, Audio Overviews, mobile apps and increasingly sophisticated research features. Google launched official NotebookLM apps for iOS and Android in 2025.

🎧 Then Came the AI Podcasts

This was the moment when many people said:

“Wait… WHAT?” 😁

NotebookLM introduced Audio Overviews — AI-generated podcast-style discussions based on your sources.

Upload an article, research paper or study materials and you can listen to an AI conversation about the content instead of reading every page.

For learning, that's surprisingly useful:

read → listen → ask → understand.

And Google kept pushing the idea further.

NotebookLM can now generate different types of outputs, including audio, video, reports, visualizations and other artifacts depending on the features and access level available to the user.

💰 And Then Came the Subscription

Here's the classic AI-product story.

First:

“Try it for free!”

Then:

“Here's another awesome feature.”

Then:

“This one is available with expanded access.” 😏

Google did launch NotebookLM Plus, offering higher limits, additional customization and team-oriented features. In 2025, NotebookLM Plus became part of Google One AI Premium.

But NotebookLM didn't simply become a paid-only product.

Free access still exists.

The difference is that Google increasingly separates capabilities and usage limits between plans.

📈 And That's Where Google's Strategy Gets Interesting

First, users try the tool.

Then they start using it regularly.

Then it becomes part of their workflow.

And eventually:

“I don't want to go back to doing this manually.”

That's when a subscription becomes much easier to justify.

You're no longer paying for a shiny new feature.

You're paying for:

saved time.

🤯 NotebookLM Is Becoming Much Bigger

This is probably the biggest change in 2026.

Google is moving NotebookLM from an AI-powered notebook toward a much more sophisticated research system.

In June, Google announced:

• more advanced reasoning;
• agentic capabilities;
• web source discovery;
• code execution;
• data analysis;
• charts;
• PDF reports;
• spreadsheets;
• presentations;
• documents;
• images;
• structured data.

So the workflow is changing from:

“Here are my documents. Help me understand them.”

to:

“Here's my problem. Help me research it, analyze it and produce the result.”

That's a major shift.

🤖 Is NotebookLM Becoming an AI Agent?

In a sense, yes.

Google explicitly describes new agentic capabilities in NotebookLM.

The system can help users start research from a loose idea, discover relevant sources and build a research repository around them.

So instead of:

“I already know which sources I need.”

you can increasingly say:

“Here's what I'm trying to figure out. Help me find the information.”

That's much closer to an AI research partner.

💻 And Then There's Code

Google has also added a secure cloud computer to NotebookLM that allows the system to write and execute code for deeper analysis.

Imagine you have a sales dataset.

The old workflow:

you → export → spreadsheet → formulas → charts → report.

The new AI-assisted workflow can automate much more of that process.

Google says NotebookLM can create charts, tables, documents, presentations and other outputs based on your project materials.

At this point, calling it “just a PDF reader” would be a little unfair.

🔗 NotebookLM Is Also Getting Closer to Gemini

Google has been connecting NotebookLM and Gemini more tightly.

In 2026, Google introduced Notebooks in Gemini — dedicated spaces for longer-running projects that sync with NotebookLM.

You can combine:

chats + files + instructions + sources + research.

And then use different Gemini and NotebookLM capabilities within the same workflow.

That's why the new name Gemini Notebook makes sense.

Google is building a broader knowledge-work ecosystem.

📱 What About the Free Version?

This is where you need to look at the specific plan.

Basic access to NotebookLM remains available, but expanded limits and certain features depend on the subscription level.

For example, Google says AI Pro provides higher limits for Audio Overviews, Flashcards, Infographics, Q&A, Quizzes, Reports, Slides and Video Overviews, along with a larger source limit per notebook.

So asking:

“Is NotebookLM free or paid?”

isn't really the best question.

A better one is:

“Which NotebookLM features are free, and which require a paid plan?”

That's the question that actually matters.

🎯 Who Should Use NotebookLM?

📚 Students

Upload study materials → summarize → generate questions → review.

🧑‍💻 Freelancers

Client sends 15 documents?

Perfect.

Let AI read the nightmare first. 😁

📊 Marketers

Combine:

• research;
• customer feedback;
• reports;
• articles;
• competitor materials.

Then look for patterns.

📰 Journalists

Collect multiple sources and compare them in one place.

💼 Business Owners

Use documentation, reports, research and data as a working knowledge base.

⚠️ There's Still a Catch

NotebookLM doesn't magically turn AI into an infallible source of truth.

Google itself warns that AI-generated content can contain inaccuracies.

So:

upload → AI answers → must be true

is not a reliable workflow.

A better one is:

upload → analyze → verify important conclusions.

Especially when you're dealing with money, legal matters, research or major business decisions.

🔐 What About Your Data?

This is another important consideration.

Google says that for consumer NotebookLM use, data isn't used to train NotebookLM unless you provide feedback. Different terms and protections apply to qualifying Workspace accounts.

Still, if you're uploading confidential client or company information, always check:

which account you're using;

sharing settings;

your organization's policies;

the terms of your specific plan.

Convenience is great.

Accidentally sharing confidential documents?

Not so much. 😁

🤔 So, Is NotebookLM Worth Using?

If you regularly work with large amounts of information — absolutely worth trying.

Its biggest advantage isn't that it's “the smartest AI ever.”

It's this:

it turns a pile of documents into a usable workspace.

And that's incredibly valuable.

🚀 And Here's Where This Is Going

Look at the progression:

PDF → AI analysis → audio → video → source discovery → code → spreadsheets → presentations → AI agents → Gemini integration.

That's no longer just a digital notebook.

And in July 2026, Google officially renamed the product Gemini Notebook, signaling an even deeper integration with the broader Gemini ecosystem.

So the question isn't really:

“Why would I need an AI notebook?”

It's becoming:

“Why am I still reading and organizing all of this manually?” 😁

🎁 Want ready-to-use AI materials?

Subscribe to the channel and head over to the Telegram bot.

You can choose one bonus or grab both:

🔥 “Universal Prompt Templates for Marketers”

💰 “8 Neural Networks for Making Money Online”

👉 Get your bonuses:
t.me/invite_bot_EN_bot

Grab both — they'll be useful for working with AI and exploring ways to monetize AI tools. 🚀

We usually think about AI like this:

“Write me a post.”

“Give me an idea.”

“Generate an image.”

“And now explain why Bitcoin is falling again.” 😁

But the really interesting part of AI starts when you move beyond fun demonstrations.

What happens when a company has to process thousands of customer messages, clean messy data, create documentation, write code or understand what customers actually want?

That's where AI stops being a toy and starts becoming a real business tool.

A recent Loginom analysis highlights five practical areas where AI can create measurable value: text and data processing, customer communication, automated documentation, code generation and recommendation systems.

Let's break them down without the corporate jargon.

1️⃣ Text and Data: Let AI Deal With the Mess

Real-world data is rarely clean.

One system says:

“New York”

Another:

“NYC”

Another:

“New York, USA.”

And then someone types:

“New Yrok.”

Good luck building a clean report from that. 😁

AI can help identify duplicates, normalize text, correct obvious inconsistencies and structure unorganized information.

This becomes particularly useful when companies deal with large volumes of CRM, ERP, customer-service and other text-based data.

AI can also extract meaning

Imagine thousands of customer complaints.

A human can read a few hundred.

Then eventually say:

“I'm going for coffee.”

AI can process much larger volumes and identify:

• recurring problems;
• common causes;
• frequently mentioned products;
• problematic assets;
• repeated complaints.

Instead of a giant pile of text, you get a structured picture of what's happening.

2️⃣ Customer Communication: Fewer “Please Contact Another Department” Moments

Imagine a customer writes:

“My equipment isn't working.”

Someone has to read the message.

Identify the issue.

Find the right department.

Forward it.

Someone else reads it again.

And only then does the actual work begin.

For a few messages, that's fine.

For thousands?

That's exactly where automation starts making sense.

AI agents can classify incoming requests and route them to the right specialists — technical teams, legal departments, accounting and so on.

💬 AI Can Also Analyze Feedback

Reviews.

Forums.

Social media.

Chats.

Comments.

For businesses, this is an enormous source of information.

The problem?

There is simply too much of it.

AI can group feedback by topic, identify sentiment and detect recurring problems.

Instead of:

“We have 48,000 comments.”

you can get:

“37% of negative feedback is about delivery, 22% about product quality and 14% about customer support.”

Now that's useful information.

3️⃣ Documentation: Maybe Someone Will Finally Write It

There are things employees love.

Friday.

Payday.

Vacation.

And documentation.

Documentation usually ranks lower. 😁

The problem is that documentation needs to be written today, while someone may need it months later.

So it often gets postponed.

And then postponed again.

And then everyone pretends it exists.

AI can help generate documentation from processes and data, organize it and make it easier to search and reuse.

Even when the AI output needs human review, starting from a solid draft is often much easier than staring at a blank page.

4️⃣ Code: Developers Can Finally Delegate Some Routine Work

AI-powered code generation has become one of the most obvious practical applications of generative AI.

Need a quick prototype?

AI can help.

Need a small data-processing script?

AI can help.

Need test cases?

AI can help with those too.

The combination of AI and low-code is especially interesting because it can allow more people to participate in development without manually writing every line of code.

But there's an important BUT.

AI is not magically error-proof.

Generated code still needs:

testing → validation → review → quality control.

Because:

“ChatGPT said it works”

is not a testing strategy. 😁

5️⃣ Recommendation Systems: When AI Starts Guessing What Customers Want

You've probably seen this:

“I just looked at this product… and now it's everywhere.”

That's recommendation technology at work.

AI can analyze user behavior and predict what a person may be interested in next.

Common applications include:

• personalization;
• cross-selling;
• upselling;
• content recommendations;
• product assortment optimization.

For businesses, this matters because recommendation systems can influence both customer experience and commercial metrics such as conversion and average order value.

⚠️ But There's a Big Trap

After reading all this, it's easy to think:

“Great! Let's add AI everywhere!”

That's exactly what you shouldn't do.

AI isn't magic.

If your underlying business process is broken, AI may simply allow you to do the wrong thing faster.

That's not exactly productivity. 😁

💸 AI Can Actually Increase Costs

Using AI where it isn't needed can create:

• unnecessary expenses;
• slower workflows;
• incorrect results;
• misleading analysis;
• data-security problems.

Loginom specifically highlights the importance of controlling AI costs, data quality and output quality.

So the better question isn't:

“Where can we add AI?”

It's:

“Where can AI solve this problem better, faster or cheaper?”

That's the question worth asking.

🧠 How Do You Know If a Task Is Good for AI?

Use a simple filter.

A task is a strong candidate when it:

happens regularly;

involves large amounts of data;

follows recognizable patterns;

has measurable outcomes;

can be monitored and checked.

For example:

❌ Generate one company name — AI is useful, but automation probably isn't necessary.

✅ Classify 10,000 customer requests every day — completely different story.

The difference is scale and repetition.

🚀 AI Shouldn't Just “Do Something”

Here's the key idea:

AI itself doesn't create business value.

Value appears when AI becomes part of a reliable process.

For example:

data → AI → validation → action → measurable result.

Not:

AI → cool answer → everyone goes home. 😁

🎯 What Can You Try Today?

If you work with data, customers or content, start with one small repetitive task.

For example:

classify customer requests;

clean text data;

group reviews by topic;

draft documentation;

generate test code;

analyze customer feedback.

Then measure the result.

How much time did you save?

Did errors decrease?

How much did it cost?

Did quality improve?

That's how you find out whether AI is actually useful.

🔥 The Bottom Line

Don't adopt AI simply because:

“Everyone else is using it.”

Use it where it genuinely removes repetitive work, speeds up processes or improves decisions.

Because the best AI tool isn't necessarily the one with the most features.

It's the one that solves your specific problem.

And ideally, it doesn't create three new problems while doing it. 😁

🎁 Want ready-to-use AI materials?

Subscribe to the channel and head over to the Telegram bot.

You can choose one bonus or grab both:

🔥 “Universal Prompt Templates for Marketers”

💰 “8 Neural Networks for Making Money Online”

👉 Get your bonuses:
t.me/invite_bot_EN_bot

Grab both — they'll be useful for working with AI and exploring ways to monetize AI tools

There are two types of people using AI.

The first type writes:

“Write a post about artificial intelligence.”

Then gets a boring wall of text and concludes:

“AI is overrated.” 😁

The second type writes:

“You are an experienced technology copywriter. Create a short Telegram post explaining how AI can save time. The audience consists of people with no technical background. Use a friendly tone and light humor. Give me 3 headline options and keep the post under 1,500 characters.”

Suddenly, the answer becomes much more useful.

The difference isn't necessarily the AI model.

It's the instruction.

🤖 What Is a Prompt?

A prompt is simply an instruction you give to an AI model.

Think of it as explaining:

what to do → why → for whom → in what format → what to avoid.

Compare:

“Write about coffee.”

with:

“Write an 800-word article about coffee for a healthy-lifestyle blog. The audience is women aged 25–40. Explain the potential benefits and limitations in simple language. Avoid medical jargon and use a friendly tone.”

The second instruction gives the AI a much clearer target.

And that's the whole point of prompting.

🧩 The Formula for a Better Prompt

A strong prompt can contain several building blocks:

1️⃣ Role

Tell the AI who it should act as.

For example:

“You are an experienced SEO copywriter.”

or:

“You are a Python teacher for beginners.”

A role helps establish the appropriate perspective, style and level of expertise.

But don't go crazy with:

“You are the greatest marketing genius who has ever lived…”

😄

Specific expertise is much more useful than superhero credentials.

2️⃣ Task

Be precise about what you actually want.

Instead of:

“Create some content.”

try:

“Create 5 Telegram post ideas for a fashion store.”

Even better:

“Create 5 Telegram post ideas for a women's fashion store. The audience is women aged 25–40. The goal is to increase website visits. Give each idea a headline and a short description.”

Now the AI has a clear assignment.

3️⃣ Context

Context is one of the most underrated parts of prompting.

Compare:

“Write an advertisement.”

with:

“Write a Telegram advertisement for a small skincare brand. The audience is women aged 25–40. The product is a moisturizing serum. Customers mainly struggle with dry skin. Keep the message natural and avoid aggressive sales language.”

Same AI.

Completely different result.

Why?

Because the second prompt gives the model the situation.

4️⃣ Output Format

Want a table?

Say:

“Return the result as a table.”

Want a list?

“Give me 10 bullet points.”

Need JSON?

“Return valid JSON.”

Need three short versions?

“Create 3 options, each under 100 words.”

This sounds obvious.

Yet people forget it all the time.

Then they ask:

“Why did you write 2,000 words?”

Because you never said not to. 😁

5️⃣ Examples

If you want a specific style, show the AI an example.

For instance:

“Here are two headline examples I like:

1.      …

2.      …
Create 10 new ones in a similar style without copying them.”

Examples can be extremely useful for:

• copywriting;
• design;
• coding;
• advertising;
• social media;
• headlines.

You're not only explaining what you want.

You're showing the AI what good looks like.

6️⃣ Constraints

Tell the AI what not to do.

For example:

“Avoid corporate language.”

“Don't use technical jargon.”

“No clichés.”

“Don't mention competitors.”

“Avoid an overly promotional tone.”

These small instructions can make a surprisingly big difference.

🧠 The Prompt Structure

A useful formula is:

ROLE → TASK → CONTEXT → FORMAT → EXAMPLES → CONSTRAINTS

For example:

Role: You are an experienced marketer.

Task: Create 5 headlines for a Telegram post.

Context: The channel covers AI tools. The audience consists of beginners.

Format: Each headline must be under 80 characters.

Examples: Use curiosity and a dynamic tone.

Constraints: No clichés, fake promises or aggressive clickbait.

Now you've given the AI an actual brief instead of:

“Make something cool.”

🔄 What If the First Result Is Bad?

Don't immediately close the tab and blame AI. 😁

The first response is often just the beginning.

Look at what went wrong.

Was the tone wrong?

Too long?

Too generic?

Missing important details?

Then refine the prompt.

For example:

“Good start, but make the text less formal. Remove corporate language and add some humor.”

Then:

“Now shorten it by 30% while keeping the main ideas.”

Then:

“Create three stronger headline options.”

That's iterative prompting.

Prompt → result → review → refinement → better result.

And often the second or third version is dramatically better.

⚡ Don't Try to Create the Perfect Prompt Immediately

Another common mistake is spending 20 minutes writing a gigantic prompt before even testing it.

Don't.

Start with a clear instruction.

Test it.

See what happens.

Then improve it.

AI isn't a vending machine with an:

“IDEAL RESULT”

button.

It's more like a very fast assistant who needs a decent brief.

🎯 A Universal Prompt Template

Save this:

You are [ROLE / EXPERT].

Your task is to [WHAT NEEDS TO BE DONE].

Context:

[IMPORTANT INFORMATION ABOUT THE PROJECT,

AUDIENCE AND GOAL].

Target audience:

[WHO THIS IS FOR].

Output format:

[HOW THE RESULT SHOULD LOOK].

Examples:

[EXAMPLES OF THE DESIRED RESULT].

Constraints:

[WHAT THE AI SHOULD NOT DO].

Before giving the final answer,

check that the result follows all requirements.

You don't need every section for every request.

A simple question may need only one or two sentences.

But for complex work, this structure can save a lot of time.

🚀 The Most Important Rule

A good prompt isn't necessarily a long prompt.

It should be:

clear;

specific;

context-aware;

structured;

focused on the result.

And most importantly:

Don't make the AI guess.

If you want something specific — tell it exactly what you want.

AI can't read your mind.

Yet. 😏

🎁 Want ready-to-use AI materials?

Subscribe to the channel and head over to the Telegram bot.

You can choose one bonus or grab both:

🔥 “Universal Prompt Templates for Marketers”

💰 “8 Neural Networks for Making Money Online”

👉 Get your bonuses: t.me/invite_bot_EN_bot

Grab both — they'll be useful for working with AI and exploring ways to monetize AI tools. 🚀

If you still look at AI and think:

“Okay… ChatGPT writes things, AI makes pictures, robots are apparently taking over the world… But what am I supposed to do with it?” 😁

Relax.

You don't need to become a programmer.

You don't need a degree in mathematics.

And you definitely don't need to start your morning talking about transformer architectures.

AI is already built into countless modern apps and devices. It can help with text, images, video, data analysis, research, education and automation.

And getting started can be as simple as solving one real problem.

🤖 So, What Actually Is AI?

In simple terms, artificial intelligence refers to technologies that allow computers to perform tasks that traditionally required human intelligence — analyzing information, recognizing speech and images, finding patterns, making predictions and generating content.

Then come the complicated words:

AI → machine learning → neural networks → deep learning → generative AI → LLMs…

Beginner:

“I just wanted help writing an email.” 😂

Let's simplify.

🧠 Machine Learning

Computers learn patterns from examples instead of being given every rule manually.

🧠 Neural Networks

A popular type of machine-learning architecture designed to handle complex patterns.

🎨 Generative AI

AI that can actually create new content:

text;

images;

music;

video;

code.

💬 LLMs

Large language models power many modern AI assistants.

And finally…

✍️ The Prompt

Your instruction to the AI.

The difference between:

“Write a post.”

and:

“Write a short Telegram post explaining the benefits of AI for beginners. The audience has no technical background. Use a friendly tone and light humor. Keep it under 1,000 characters.”

is enormous.

The second one gives the AI an actual assignment.

💡 What Can AI Actually Do?

Quite a lot.

And no, it's not just a machine for generating cute cats.

Although it is surprisingly good at cats. 😁

✍️ 1. Work With Text

AI can help you:

• brainstorm ideas;
• write drafts;
• correct grammar;
• rewrite text;
• shorten content;
• translate;
• adapt content for different platforms;
• create headlines;
• write emails.

For example:

“Rewrite this email to sound more confident and friendly while making it 30% shorter.”

That's potentially 20 minutes of editing you don't have to do manually.

🎨 2. Create and Edit Images

Modern AI tools can generate images from descriptions and edit existing pictures.

You can ask for:

“A realistic cat wearing a chef's uniform in a modern kitchen.”

And suddenly you have a cat.

In a chef's uniform.

Looking suspiciously confident. 😁

AI can also:

• remove objects;
• change backgrounds;
• expand images;
• generate missing areas;
• create illustrations;
• produce visual concepts.

🎓 3. Learn

This is one of the most useful applications.

Instead of searching through ten websites trying to understand something complicated, ask AI to explain it at your level.

For example:

“Explain quantum entanglement as if I'm 10 years old. Use an everyday analogy.”

Still confused?

“Make it even simpler.”

Still confused?

“Give me a real-life example.”

AI can become a surprisingly patient personal tutor.

And unlike a human teacher, it doesn't get annoyed when you ask:

“Okay… but explain that again.” 😁

📊 4. Analyze Information

Large document?

Spreadsheet?

Customer feedback?

Long report?

AI can help:

• summarize information;
• organize data;
• identify patterns;
• compare documents;
• extract key points;
• create structured conclusions.

But there's an important warning:

AI can be wrong.

And it can be wrong while sounding extremely confident.

These errors are commonly called AI hallucinations. Important facts, numbers, names and dates should always be checked.

💼 5. Use AI at Work

This is where things get really interesting.

Marketers

Ideas, campaigns, copy, audience analysis.

Designers

Images, concepts, references and visual variations.

Developers

Code generation, explanations, prototypes and tests.

Entrepreneurs

Market research, documents, brainstorming and business ideas.

Students

Explanations, summaries and learning materials.

Everyone else

Finally getting that annoying email written in five minutes instead of one hour. 😁

🚀 How Should a Beginner Start?

Here's where people often make a mistake.

They search:

“100 best AI tools in 2026.”

Then open:

ChatGPT.

Claude.

Gemini.

Perplexity.

17 more tabs.

And after two hours:

“Okay… but what do I actually use?” 😂

Don't do that.

1️⃣ Start With One Problem

Don't say:

“I want to learn AI.”

Say:

“I want to write articles faster.”

or:

“I need to create images for social media.”

or:

“I want to analyze documents.”

or:

“I want to automate repetitive tasks.”

That's a much better starting point.

2️⃣ Choose One Tool

You don't need 25 AI services on day one.

Pick one general-purpose assistant.

Use it.

Give it a real task.

See what happens.

Then expand your toolkit.

3️⃣ Learn How to Write Prompts

This is one of the most valuable AI skills.

Don't write:

“Make it better.”

Try:

“You are an experienced Telegram editor. Rewrite this text in simple conversational English. Remove corporate language, keep the meaning and shorten it by about 30%.”

The clearer your instructions, the less the AI has to guess.

4️⃣ Don't Be Afraid to Correct the AI

Bad answer?

Don't immediately start over.

Say:

“Too formal.”

Or:

“Make it shorter.”

Or:

“Add examples.”

Or:

“You forgot the part about delivery.”

Or:

“Rewrite the third option.”

This isn't an exam.

You don't have to accept the first answer as if it were carved in stone.

AI is a conversation.

⚠️ A Few Things You Should Never Forget

🔐 Don't Share Sensitive Information

Avoid putting these into public AI services:

• passwords;
• payment information;
• customer personal data;
• trade secrets;
• confidential documents;
• private API keys.

Even if the chatbot asks nicely. 😁

Check the service's privacy and data-processing policies before using it for sensitive work.

🧐 Verify Important Information

AI can sound extremely convincing and still be wrong.

For:

• finance;
• medicine;
• law;
• important business decisions

AI should be an assistant, not the final authority.

Humans remain responsible for important decisions.

⚖️ Think About Copyright

The fact that an AI generated something doesn't automatically mean you can use it anywhere without restrictions.

Different services have different terms.

This matters especially for commercial work.

Check the specific tool's rules before publishing or selling generated content.

🔮 What's Coming Next?

We're probably still near the beginning.

AI is moving from simple chatbots toward systems capable of completing multi-step tasks.

Instead of:

“Answer this question.”

you'll increasingly be able to say:

“Research the options → compare them → prepare the result → create a document → organize everything.”

This is where AI agents come in.

At the same time, multimodal systems are combining text, images, audio and video.

And we're likely to see more specialized AI systems for areas such as medicine, finance, law and education.

🎯 The Main Thing to Remember

You don't need to learn all of AI.

You don't need to understand the mathematics behind neural networks.

You don't need programming skills.

You don't need 50 AI subscriptions.

And you don't need to wait until you're “ready.”

Start with one real problem.

Write one useful prompt.

Get your first result.

Try again.

Improve it.

Eventually, AI stops looking like some mysterious futuristic technology.

It becomes a normal tool.

Like a browser.

Like a smartphone.

Like Excel.

Just much more talkative. 😁

The future of AI is already here. Now it's time to learn how to use it. 🚀

🎁 Want ready-to-use AI materials?

Subscribe to the channel and head over to the Telegram bot.

You can choose one bonus or grab both:

🔥 “Universal Prompt Templates for Marketers”

💰 “8 Neural Networks for Making Money Online”

👉 Get your bonuses:
Telegram bot for bonuses

Grab both — they'll be useful for working with AI and exploring ways to monetize AI tools. 🚀