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Working with neural networks is no longer a privilege for IT specialists. Today, generative AI helps create content, solve work tasks, write code, and even earn money. This doesn't require deep technical knowledge or programming skills. It's enough to choose the right tool, understand the basic principles, and make your first query. Most beginners face the same problem: where to start, which platform to choose, how to write a prompt to get a good answer. This article breaks down specific steps, examples, and common mistakes.

Why Everyone Should Know How to Work with Neural Networks Today: Numbers and Opportunities

The generative AI market is showing triple-digit growth. In 2024, the total revenue of leading platforms exceeded $50 billion. Analysts predict the figure will double by 2026. This is not fantasy, but a reality that is changing the rules of work in marketing, design, development, and other fields.

How Much You Can Earn by Mastering Neural Networks: Real Market Figures

A freelancer proficient in Midjourney and Stable Diffusion earns $150-300 for a logo. An SMM specialist using ChatGPT for content plans speeds up work by 3-4 times and takes on twice as many clients. A copywriter generating texts through Claude increases revenue by 40-60% due to higher order volume.

The Russian market shows similar trends. Job postings for "Neural Network Specialist" appear 5-7 times more often than a year ago. Salaries start from 80,000 rubles for juniors and reach 300,000+ for experts who integrate AI into business processes. The IMI platform allows you to start without investment: the free plan includes 12,000 words monthly, equivalent to 15-20 medium-sized articles.

Entrepreneurs who implement neural networks into their work reduce content costs by 50-70%. Product cards for marketplaces, service descriptions, social media posts – all of this is generated in minutes, not hours. Time saved translates into direct profit: freed-up resources are directed towards scaling and attracting clients.

How neural networks are changing professions: who wins, who loses.

How Neural Networks Are Changing Professions: Who Wins, Who Loses

Copywriters, designers, and SMM specialists are actively using AI. The technology doesn't replace professionals but enhances their capabilities. Those who quickly master the tools gain a competitive advantage and increase their market value.

Technical specialists gain new opportunities. Programmers use GitHub Copilot to generate code, saving 30-40% of time on routine tasks. Testers use AI to create test cases, analyzing results 2-3 times faster. Data scientists process massive datasets in minutes, not hours.

Marketers and content managers expand their competencies. Generating articles, posts, and ad creatives speeds up 4-5 times. At the same time, specialists focus on strategy, analytics, and creativity – tasks that require human thinking. The result: salaries grow by 50-80% per year.

Professions at higher risk: routine document processing, basic technical support, simple layout. Here, AI replaces 70-80% of operations. However, even in these niches, there is room for quality control, process setup, and handling exceptions.

Adapting is simple. Start by learning one tool, for example, IMI or ChatGPT. Practice daily: write prompts, analyze answers, adjust queries. In 2-3 weeks, you'll feel confident. In 2-3 months, you'll be able to integrate AI into your main workflows.

Professions of the future require flexibility. The ability to work with neural networks is becoming a basic skill, like knowing Excel 10 years ago. Those who start now gain the early adopter advantage and solidify their leadership in their niche.

Neural Networks for Beginners: What They Are and How They Work (Without the Fluff)

A neural network is a program that learns from a large amount of data and can identify patterns. Unlike regular code where every action is predefined, a neural network independently builds connections between input information and the desired result. The process is similar to teaching a child: you show thousands of examples, and the system begins to understand what is expected of it.

The principle of operation is simple. You input a query (text, image, question), the neural network analyzes it through layers of neurons and generates a response. Each layer is responsible for a certain level of abstraction: one recognizes letters, another words, a third the meaning of a phrase. The result seems "smart," but it is actually a statistical model predicting the next word or pixel.

3 Main Principles of Neural Networks Every Beginner Should Know

First principle – learning from data. The neural network doesn't know the absolute truth. It remembers billions of examples from the internet, books, databases and builds responses based on them. If there is little information on your topic in the training data, the result will be superficial. Therefore, specialized tasks require models with deep expertise in narrow fields.

Second principle – tokens. Text neural networks don't work directly with letters. They break down the query into tokens – conditional fragments 3-4 characters long. All models have token limits: free versions have 4-8 thousand, paid versions have up to 128 thousand. This is important because a long text won't fit into the query, and part of the information will simply be "unseen" by the system.

Third principle – context window. The neural network only remembers what fits into the current dialog. If you start a new chat, previous messages are erased. For working with large documents, use the file upload function or extended prompts where important data is specified at the beginning of each query.

Types of Neural Networks: Where to Find Text, Graphic, and Which to Choose for Starting

Text models – the most popular. ChatGPT, Claude, DeepSeek generate texts, answer questions, write code, analyze documents. For a beginner, it's better to start with one of these platforms. IMI combines several such models in one window, allowing you to compare answers and choose the best option.

Graphic neural networks create images. Midjourney, Stable Diffusion, DALL·E, Flux – are the main tools. Image generation requires a different approach: you need to specify style, details, composition. Beginners find it easier to start with Midjourney via Discord or with built-in tools in IMI.

Video neural networks – the newest. Runway, Pika, Synthesia, Kling generate short videos, animations, avatars. Currently, these tools are expensive and require powerful hardware, but they are developing rapidly. To start, it's enough to try free demos to understand the potential. For business, video avatars already save thousands of dollars on filming.

Step-by-Step Plan: How to Start Working with a Neural Network in 30 Minutes

Practice shows: half an hour after the first query, a beginner already understands if the tool works. The main thing is to choose the right platform, set up an account, and formulate the task. The algorithm consists of four steps. Each takes 5-10 minutes. Follow sequentially, and in 30 minutes you'll get your first ready result.

Step 1: Choosing a Platform – Where to Register and What Free Options Exist

IMI – a platform that combines GPT‑5, Claude, Midjourney, Flux, and other models in one interface. Registration via email or phone, free plan with 12,000 words. Suitable for starting without investment.

DeepSeek – a Chinese model that bypasses many Western restrictions. Shows good results in code and analytics. A free plan is available, registration via email.

GetAtom – a neural network aggregator, similar to IMI. Offers access to several models, business templates, a free trial period. Convenient for comparing the quality of different AI responses.

Registration takes 2 minutes. Enter your email, create a password, confirm via SMS or code. It's important to immediately check the free limit: how many words/queries are available, which models are included. Some platforms give a bonus for the first week – use it to test all functions.

After registration, proceed to profile setup. Specify your field: marketing, design, development, education. This helps the system select suitable templates and assistants. In IMI, you can immediately choose a ready-made AI assistant for your niche – saves time learning the interface.

Step 2: Registration and Initial Setup – What Must Be Done

After creating an account, the system will suggest setting up your profile. This takes 3-4 minutes but affects work convenience. Choose your field from the list: marketing, design, development, education, e‑commerce. The platform will select suitable templates and assistants.

Check the free limit. In IMI – 12,000 words per month; in DeepSeek – token limit. Write down the numbers to track usage. If you plan large volumes, study the paid plans immediately: paid versions give access to more powerful models and a larger context window.

Configure the interface. In the profile, you can choose a theme (light/dark), default language, response format. IMI has a voice input function – activate it if dictating queries is convenient. Specify preferences for answer length: short summaries or detailed texts.

Choose an AI assistant. The IMI platform offers 30+ ready-made assistants: "Marketer," "SMM Expert," "Copywriter," "Data Analyst." Each assistant is already configured for a specific style and tasks. Beginners should start with the "Universal Assistant" – it gives balanced answers to any questions.

Prepare your workspace. Create a folder for saving results, open a text editor for prompts. If you plan to work with large documents, upload them to the system in advance: PDFs, spreadsheets, presentations. IMI allows training the model on your own data – useful for specialized tasks.

Step 3: First Prompt – How to Write a Query to a Neural Network Correctly

A prompt is an instruction for AI. The quality of the result depends on how accurately you formulate the task. Beginners often write short phrases like "write text" and get a template answer. To get a useful result, you need to give the neural network context, specify format, tone, and constraints.

A good prompt consists of 4 parts: role, task, format, constraints. For example: "You are a professional copywriter (role). Write a sales description for a coffee shop in central Moscow (task). Length – 200 characters, style – friendly, without filler words (format and constraints)." Such a query gives a specific result that can be used immediately.

5 Simple Prompt Templates That Work the First Time

Template 1: "You are an expert in [field]. Write [type of text] for [target audience]. Length – [number of words/characters]. Tone – [professional/friendly/ironic]. Avoid [what is not needed]." Example: "You are an SMM expert. Write 5 headline options for a post about discounts on neural network courses for Instagram followers. Length – up to 80 characters. Tone – energetic, without exclamation marks."

Template 2: "Create [number] variants of [what]. Each variant should [feature]. Format – [list/table/text]." Example: "Create 3 variants of a course description 'How to work with neural networks from scratch.' Each variant should emphasize benefits for beginners. Format – list with 3 items."

Template 3: "Analyze [data]. Highlight [what to look for]. Present the result as [format]." Example: "Analyze feedback on a neural network course. Highlight 3 main pain points of students. Present the result as a table: pain point – quote – solution."

Template 4: "Rewrite [text] for [purpose]. Make [what changes]. Keep [what to leave]." Example: "Rewrite this prompt for beginners, make it simpler, remove technical terms. Keep the structure: role – task – format."

Template 5: "Suggest ideas for [what]. Quantity – [number]. Each idea should include [details]." Example: "Suggest 5 article ideas about working with neural networks from scratch for a blog. Each idea should include a title, main intent, approximate length."

Test templates immediately. Open the platform, copy a template, insert your data. Compare results from different models. Note which prompts gave the best answer. After 10-15 attempts, you'll start to feel which formulations work better.

A beginner's mistake – overly general queries. "Write about marketing" gives a watery text. "Write 5 theses for a presentation on implementing neural networks in an SMM strategy for a travel agency" – gives specifics. Specificity is the key to quality.

Step 4: Generating Your First Content – From Idea to Result in 5 Minutes

The first task should be simple and give quick feedback. Take a real scenario from your work. An SMM specialist can generate 3 headline options for a post. A marketplace owner – a product description. A copywriter – intro options for an article. A clear task helps evaluate answer quality.

Open the platform, choose a model. For text, GPT‑5 is suitable; for images – Midjourney or Flux. Paste the prompt from the template, insert your data. Click "Send" and wait 10-30 seconds. The system will return the result.

Compare the answer with expectations. If the text is too general, add details to the prompt. If the image isn't right, clarify style, colors, composition. The first result is rarely perfect. The main thing is to see how the answer changes when the query is adjusted.

Save the obtained content in a separate file. Mark which prompt gave the best result. This creates your library of effective queries. After 10-15 generations, you'll collect a set of working templates you'll use constantly.

How to Work with Neural Networks from Scratch in Different Niches: Practical Cases

Theory without examples doesn't work. Let's examine specific scenarios for three segments: SMM specialists, marketplace owners, copywriters. Each case includes a ready prompt, expected result, and tips for improvement. You can immediately copy the template, insert your data, and test in practice.

For an SMM Specialist: A Monthly Content Plan in 1 Hour

Scenario: need to create 30 Instagram posts about tourism. Prompt: "You are an SMM expert for a travel agency. Create a content plan for 30 days. Each post should contain: a title (up to 60 characters), main text (up to 1500 characters), 5 hashtags, a call to action. Tone – energetic, friendly. Topic examples: last-minute tours, customer reviews, traveler tips." The model generates a table with 30 rows. Check each post for brand compliance. If a title is too general, add a clarification to the prompt: "focus on budget travel to Turkey and Egypt."

For a Marketplace Owner: Product Cards That Sell

Scenario: selling coffee tables on Wildberries. Prompt: "You are a copywriter for a marketplace. Write a product description: coffee table made of solid oak, 80x80 cm, moisture-resistant coating, weight 15 kg. Length – 1000 characters. Structure: 3 benefits at the beginning, detailed specifications, care advice. Tone – expert, no fluff. Add 3 title options up to 50 characters." The system outputs a ready description. Check for marketplace requirements. If keywords are needed, add to the end of the prompt: "Include keywords: oak table, living room furniture, coffee table."

For a Copywriter: How Not to Lose Your Job and Earn More with AI

Scenario: writing a blog article about neural networks. Prompt: "You are a copywriter with 10 years of experience. Write an introduction for the article 'How to work with neural networks from scratch.' Length – 300 characters. Tone – expert but accessible. Must include a metaphor that explains the complex in simple terms. Avoid clichés." Get 3 variants. Choose the best, edit to your style. Important: AI gives a draft that needs to be perfected. It's not a replacement, but an acceleration.

Repeat the process 5-7 times for different tasks. Note which prompts give the best results. Create your own template library. After a week of active practice, you'll generate content 3-4 times faster than before using AI.

Safety and Ethics: How to Work with Neural Networks Without Breaking the Rules

Using AI requires following certain rules. Users often ignore confidentiality, copyright, and data storage issues. This leads to leaks of commercial information, claims from clients, and loss of trust. Let's examine key points to avoid problems.

Where Your Data Is Stored and How to Protect Trade Secrets

Platforms like IMI, DeepSeek store queries on servers. Terms of use usually permit query analysis to improve models. This means: confidential data, client databases, strategies should not get into queries. Never upload files with client personal data, passwords, financial reports into a neural network.

For working with sensitive information, use local solutions. Ollama allows running models on your own computer, fully controlling data. The version with 7-13 billion parameters works on modern laptops without internet. An alternative – corporate plans on IMI, where data is processed in an isolated environment.

The rule is simple: if information could harm your business if leaked – don't send it to a cloud neural network. For public tasks – content generation, ideas, analysis of open data – the cloud is safe. For confidential data, use offline solutions or encrypted corporate access.

Can You Sell Content Created by a Neural Network: Legal Aspects

Legislation in this area is unclear. In Russia, copyright protects only works created by a human. Content generated by AI has no author in the classical sense. This means: you are not violating anyone's rights by using such content, but you also cannot register it as your intellectual property.

For commercial use, check platform licenses. IMI, DeepSeek permit commercial use of generated content. Midjourney and Stable Diffusion have restrictions: the free version of Midjourney gives a CC license, the paid version – full commercial rights. Stable Diffusion is completely free.

An important nuance: if a neural network reproduces someone else's work (copies a specific artist's style, uses protected elements), it can lead to claims. Always check the result for uniqueness. For critical tasks (logos, brand books, ad campaigns) add human refinement. This creates originality and protects against claims.

Common Beginner Mistakes: How Not to Waste Time and Money

Beginners make the same mistakes, wasting hours on useless queries. Understanding typical pitfalls saves weeks of frustration. The listed mistakes occur in 80% of beginners trying to work with neural networks without preparation. Avoid them – and results will come from the first attempts.

7 Mistakes When Writing Prompts That Kill Results

First mistake – overly general formulations. Query "tell me about marketing" gives a watery text without specifics. The system doesn't understand what's important: theory, cases, tools, numbers. The fix is simple: add details. "Tell me about an SMM strategy for a coffee shop with a 50,000 ruble monthly budget, specify 3 channels, give post examples" – such a prompt returns an actionable plan.

Second mistake – ignoring the role. A prompt without specifying "who you are" gives a mediocre answer. The neural network doesn't know who to write as: a student, CEO, freelancer. Specify the role at the beginning: "You are an experienced targetologist with 5 years of practice in e‑commerce." The answer immediately becomes expert, with appropriate terminology and depth.

Third mistake – lack of format. "Write a lot" – is not an instruction. Specify exactly: "5 headline options of 60 characters each, each with an emoji, without exclamation marks." The model loves structure. The more specific the constraints, the closer the result to expectations.

Fourth mistake – overloading one query. Beginners write 500-word prompts, trying to fit everything at once. The model loses the thread, the answer becomes chaotic. Break down complex tasks into stages. First "make an article outline," then "write the introduction," then "add examples." Sequence yields quality.

Fifth mistake – forgetting context. If working with a large document, repeat key data in each query. The neural network only remembers the current dialog. "Based on the previous answer, add 3 B2B cases" – such a phrase maintains coherence.

Sixth mistake – accepting the first answer as final. Professionals always refine. Got text? Ask to "make it more friendly, remove bureaucratese, add a metaphor." Repeated iterations turn a draft into finished material.

Seventh mistake – copying prompts without adaptation. Ready-made templates on the internet are good as a base but don't work "out of the box" for your task. Always add specifics: niche, brand, target audience. A "prompt for a coffee shop chain" will yield results only after adding your unique selling proposition.

When a Neural Network Can't Handle It: Tasks Where You Can't Do Without a Human

Neural networks don't replace humans in tasks requiring creative breakthroughs. Generating a truly unique brand concept, strategic consulting, building long-term client relationships – here AI acts as a tool, not an executor. A human sets the direction, AI speeds up implementation.

Precise calculations and auditing – another weak spot. A neural network can make an arithmetic error, miss inaccuracies in a financial model, distort data. Always double-check numbers, formulas, legal wording. AI is an assistant, not the final controller.

Ethics and empathy remain with humans. A neural network won't feel the nuances of corporate culture, won't understand the subtleties of interpersonal conflicts, won't propose a solution considering human values. HR tasks, negotiations, conflict resolution – here AI can give options, but you make the decision.

What's next: a plan for developing neural network skills.

Mastering the basics is the first stage. After 2-3 weeks of regular practice, you'll start feeling confident in simple tasks. The next step – systematic skill development. Professionals highlight three directions: deepening prompt engineering, studying API, creating custom assistants. Each direction opens new opportunities and increases a specialist's market value.

From Beginner to Pro: What to Study After Mastering the Basics

Prompt engineering – the first direction. Basic templates give results but don't reveal full potential. Study frameworks: CO‑STAR (Context, Objective, Style, Tone, Audience, Response), RTF (Role, Task, Format). These models help structure queries and get predictable answers. Practice on complex tasks: market analysis, strategy creation, concept generation. The more experiments, the better your "feel" for the right formulation.

API and integrations – the second direction. Most platforms provide an API: DeepSeek, IMI. Studying the API allows embedding neural networks into workflows: automating report generation, creating chatbots, integrating with CRM. Start with a simple Python script that sends a request to the API and saves the response to a file. Examples are abundant in platform documentation. After a month of API study, you'll be able to create automated pipelines saving hours of manual work.

Creating custom assistants – the third direction. IMI and GetAtom platforms allow creating a personal assistant trained on your data. Upload your company's knowledge base: texts, reports, presentations. Configure the role and response style. Get an assistant that responds like your best employee but works 24/7. This improves customer service quality and reduces team workload.

TOP‑5 Courses on Neural Networks for Advanced Users

Course "Creating AI Assistants" within the X10AI challenge from IMI – a module inside a 4‑day intensive. Teaches assistant setup, data upload, fine‑tuning. Available to challenge participants on Telegram. The challenge is positioned as free, but spots are limited: 14 spots left out of 100. Participants have a chance to win a MacBook or iPhone.

Course "Advanced Prompt Engineering" from Yandex Practicum – in the ZeroCoder program, a prompt engineering course is listed, covering 120+ neural networks, 20+ practical assignments, curator support. Online format, duration 2 months. Price not specified, free consultation with an expert available.

Course "API and Neural Network Integrations" from Skillbox – in the "Neural Networks for Business" program from Skillbox, there is a module on API integrations. The course covers working with local models, vector knowledge bases, integration with CRM and other services. Duration 2 months, 9 AI projects for the portfolio. Price not specified in search results.

Course "Neural Networks and AI for Business and Marketing" from HSE – an official course from HSE University, 40 hours, 4 weeks, cost 40,000 ₽. Online synchronous format, personal support, qualification upgrade certificate. The course is not free, as I mistakenly stated earlier. This is a serious program for specialists, not for beginners.

Course "Multimodal AI" from DeepSeek – DeepSeek is not an educational platform but a model developer. Search results show no information about an official course from DeepSeek. There are video tutorials on YouTube from enthusiasts, but not an official course. This is my mistake.

Conclusion: Are You Ready to Start Working with a Neural Network Right Now?

We've gone from theory to practice. Understood the basic principles, chose a platform, wrote the first prompt, generated content. The last step remains – to draw a conclusion and determine next actions. A checklist will help assess readiness. Answer 5 questions honestly. The result will show if you should start today.

Main Questions About Working with Neural Networks

Question: How long will it take to learn to work with neural networks at least at a basic level?

Answer: 2-3 hours of theory and 5-7 practical queries. 30 minutes after registration, you'll already get your first result. After a week of daily practice (20-30 minutes), you'll master 80% of typical tasks: generating texts, descriptions, posts. Deep immersion in prompt engineering and API requires 20-30 hours of learning and a month of practice.

Question: Do you need to know programming to work with neural networks?

Answer: No. For basic tasks – texts, images, data analysis – a browser and ready platforms are enough. Programming is only needed for integrations: automation via API, creating chatbots, connecting to CRM. But that's the second stage. Start without code, master prompts, understand the logic of AI work. Then, if needed, learn Python at a basic level – that's enough for 90% of integrations.

Question: What to do if the free limit runs out quickly?

Answer: Plan your queries. 12,000 words in IMI – is 15-20 medium‑sized articles. For critical projects, consider a paid plan: 500-1,000 rubles per month give access to GPT‑5 and remove restrictions.

Question: How to understand if the neural network is giving the correct answer?

Answer: Check facts. Neural networks can "hallucinate," especially with numbers, dates, links. Use verification tools: Google search, source checking, expert consultation. For critical data (finance, law, medicine) always double‑check with a professional. AI is an assistant, not the sole source of truth.

Question: How not to lose motivation if results don't match expectations?

Answer: Start with simple tasks where mistakes aren't critical. Generate post ideas, headline options, product descriptions. Success in simple tasks builds confidence. Gradually complicate your queries. Keep a log: write down the prompt, result, what you liked, what to fix. After a week, you'll see progress. The key is regularity, not perfection from the first try.

First step right now: register and make your first query to a neural network

Choose the platform – IMI. Registration takes 2 minutes: email, password, confirmation code. You immediately get 12,000 words. Go to the chat, choose the GPT‑5 model. Copy the first template from the article: "You are an SMM expert. Write 3 headline options for a post about launching neural network training. Length – up to 60 characters, tone – energetic." Paste, click "Send." In 10 seconds, get the result. Save it to a file. Repeat 5 times with different tasks. You've recorded your first success. You are already working with a neural network.

Creating AI bots involves developing chatbots that can handle user queries, understand natural language, analyze data, and generate automated responses. Today, such solutions are widely used in business, marketing, education, Telegram channels, blogs, and customer support services.

Thanks to advancements in artificial intelligence, GPT language models, and user-friendly platforms, anyone can create an AI bot—no programming required. These bots can answer questions, assist customers, process messages, generate text and images, and operate 24/7 without human intervention.

In this guide, we’ll break down the process of creating an AI bot, integrating ChatGPT, configuring prompts, leveraging content generation, and exploring real monetization strategies.

Contents

• What Is an AI Chatbot?
• How to Monetize an AI Chatbot
• How to Integrate AI into Your Chatbot
• Integrating ChatGPT into Your Bot
• Configuring ChatGPT Queries
• Promoting Your AI Bot
• Potential Earnings from an AI Bot
• Why You Can Build an AI Bot Yourself
• FAQ

What Is an AI Chatbot?

An AI chatbot is a program that interacts with users via chat, utilizing machine learning and natural language processing technologies. Unlike rule-based bots, AI chatbots understand context, clarify questions, and provide more accurate responses.

These bots are powered by GPT language models, which analyze text messages, compare them with trained data, and generate relevant replies. They can be deployed on websites, Telegram, other messengers, or via API integrations.

Creating an AI bot typically involves:

• Setting up conversation logic
• Integrating an AI model
• Uploading a knowledge base
• Testing and launching

The result is a tool that automates user interactions and solves business challenges.

How to Monetize an AI Chatbot

An AI chatbot is more than just a helper—it’s a full-fledged income-generating tool. Below are key areas where AI chatbots can drive revenue.

For Influencers

Influencers often receive repetitive questions from followers or offer free content in exchange for subscriptions or comments.

An AI bot can:

• Automatically answer FAQs
• Send direct messages with links
• Process applications
• Engage audiences across social networks

This saves time, prevents lost opportunities, and enhances the sale of paid content, consultations, and ads—while boosting follower loyalty through quick responses.

For Info-Business Owners

In the info-business space, AI bots can automate courses, training, and student support. Bots can:

• Send lessons
• Check assignments
• Answer questions
• Provide post-purchase follow-up

This reduces team workload and improves service quality, though human oversight remains essential for high-value packages.

For Marketers, Producers, and Promotion Specialists

Marketers use AI bots to:

• Process inquiries
• Analyze user requests
• Generate ad copy and scripts
• Automate customer responses and data collection
• Assist with target audience analysis

For AI Experts and Coaches

Experts and coaches deploy AI bots as personal assistants to help users:

• Navigate topics
• Ask questions
• Receive consultations
• Access learning materials in a convenient format

For Entrepreneurs

AI bots often serve as the first line of customer support, handling FAQs, assisting with orders, clarifying details, and escalating complex cases to managers. Many businesses already use bots to automate routine inquiries efficiently.

For Specialized Content Creators

If you have a database of articles, courses, or educational materials, an AI bot can act as an intelligent search tool, helping users find relevant information and navigate both archived and current content with ease.

For Telegram Channel Owners

Telegram AI bots are used for:

• Delivering content
• Processing payments
• Engaging subscribers
• Automating broadcasts

They’re a scalable tool for growing channels and maintaining audience connections.

How to Integrate AI into Your Chatbot

Integrating AI transforms your bot from a button-based script into a smart assistant that understands questions, processes messages, and leverages knowledge bases. Most platforms offer AI integration via a dedicated step (e.g., “AI block” or “GPT step”).

Step 1: Add an AI Step in the Constructor

1. Open your project dashboard and select your bot.
2. Navigate to the scenario editor (often labeled “Scenario,” “Dialogue,” “Constructor,” “Flow,” or “Funnel”).
3. Click “Add Block” (+).
4. Choose the AI step (under categories like “AI,” “Integrations,” “Text,” or “AI Response”).
5. Select the GPT model (more powerful models offer better quality but higher token costs).
6. Define the query source: user message, template, or hybrid mode.

Step 2: Configure the AI Step

Phase 1: Define the Bot’s Role and Communication Style

Specify:

• Who the bot assists (clients, subscribers, students)
• Tasks it performs (sales, support, navigation)
• Limitations (no fabrication, no unsafe advice)
• Response format (lists, steps, concise/detailed)

Tip: To prevent hallucinations, instruct the bot to respond only based on the knowledge base or ask for clarification if data is missing.

Phase 2: Set Up the Model Query

A well-structured query includes:

1. Instructions (role + rules)
2. Context (product/service details, terms, links, pricing)
3. User message (the actual question)

Add constraints like:

• “Answer accurately”
• “Ask clarifying questions if data is insufficient”
• “Avoid jargon”
• “Provide concrete steps”

Phase 3: Connect Data Sources and Knowledge Base

Without data, AI bots respond generically. Connect:

• Website text (FAQs, service descriptions)
• Documents (PDFs, manuals, price lists)
• Tables (tariffs, product specs)
• CRM or internal systems
• Google Docs/Notion

Choose between:

1. Simple knowledge base (manual text input)
2. Advanced RAG system (search + retrieval for precise answers)

Ensure data is up-to-date, categorized, and includes fallback rules.

Step 3: Test Thoroughly

• Test common questions (pricing, ordering, contact details)
• Test ambiguous or poorly phrased queries
• Verify clarifying question prompts
• Check safety and data privacy
• Optimize response time and token usage

Integrating ChatGPT into Your Bot

How to Connect ChatGPT

1. Obtain an API token (key) from OpenAI.
2. Enter the token in your service settings (“API Key” or “Access Token”).
3. Select the GPT model version.
4. Configure parameters:

• Max response length (token limit)
• Temperature (creativity level)
• System role and rules
• Response language

5. Send a test message to verify the connection.

Important: Monitor token costs, log interactions, handle errors gracefully, and enforce safety policies.

Configuring ChatGPT Queries

A well-structured query ensures consistent, useful responses.

Query Components:

1. Bot Role – Define type, scope, responsibilities, and limitations. Example: “You are a customer support bot for an online service, answering only based on provided information.”
2. Context & Conditions – Describe the environment (company, services, rules) to avoid guesswork.
3. Communication Style – Specify tone, length, simplicity, and use of emojis.
4. Response Format – Use lists, step-by-step instructions, or summaries for consistency.

Workflow Example:

1. User sends a message.
2. Message is passed to the AI step.
3. ChatGPT processes the full query (role + context + user input).
4. Model generates a response.
5. Bot delivers the answer in seconds.

Saving ChatGPT Responses

Store responses to:

• Analyze frequent questions
• Optimize knowledge bases
• Reduce model load (save tokens)
• Monitor quality and correct errors

Log interactions in databases, CRM systems, or analytics tools for ongoing improvement.

Using Image and Text Generators

Image Generation

1. Provide a detailed text description (subject, style, colors, format).
2. Send the description to an image-generation model (e.g., DALL·E).
3. Receive and deliver the generated image. Use cases: banners, article covers, product cards, social media visuals.

Text Generation

1. User specifies text type (article, product description, script).
2. Bot clarifies parameters (topic, length, style, audience).
3. Query is sent to ChatGPT with all constraints.
4. Generated text is returned, ready for use or editing.

Use cases: blog posts, service descriptions, email campaigns, dialogue scripts.

How to Start Earning with an AI Bot

Identify the problem your bot solves, its target audience, and what users are willing to pay for.

Monetization Models:

1. Subscriptions & Paid Access – Users pay for ongoing access (monthly/annually). Ideal for Telegram bots, support services, and educational projects.
2. Premium Features – Free basic functionality with paid upgrades (e.g., more queries, advanced GPT models, image generation).
3. Consultations & Services – Bot acts as a pre-consultation tool, collecting data and preparing users for paid expert sessions.
4. Advertising & Affiliate Offers – Integrate relevant ads or partner offers for large user bases. Ensure ads are contextually appropriate.
5. Sales of Products/Services – Use bots for product consultation, selection assistance, order processing, and handoff to sales teams.

Promoting Your AI Bot

Channels for Promotion:

• Website/Landing Page – Explain features, use cases, and benefits.
• SEO Content – Target keywords like “creating AI bots,” “AI chatbot for business,” “Telegram bot with AI.”
• Telegram & Messengers – Showcase bot functionality in relevant channels.
• Advertising – Use targeted ads highlighting speed, automation, or customer support.
• Integrations & Partnerships – Collaborate with platforms, services, or blogs to reach wider audiences.

Potential Earnings from an AI Bot

Income depends on niche, user base, monetization model, and promotion efforts.

• Small Telegram bot with subscriptions: $200–$500/month
• Business/support bot: $1,000–$3,000/month
• Niche AI assistants/educational bots: $5,000+/month

Note: Success requires continuous optimization, scenario refinement, and active promotion.

Why You Can Build an AI Bot Yourself

Modern no-code platforms enable anyone to:

• Create AI bots without programming
• Use pre-built templates
• Integrate ChatGPT via API
• Configure scenarios in visual editors
• Upload knowledge bases
• Launch quickly

Most services offer guides, documentation, and support. The key is to define your bot’s purpose, audience, and use case clearly.

FAQ

Can I create an AI bot for free? Yes—many platforms offer free plans or trial periods to test your idea.

How long does it take to create an AI bot? You can build and launch a basic bot in minutes using a constructor.

Do I need programming skills? No—most platforms provide intuitive interfaces and drag-and-drop blocks.

One of the most impactful January updates on the IMI service is Kling 2.6 Motion Control. It literally lets you control a character's movement frame by frame, transferring actions from real video to a static image. Previously, this level of editing required a filming crew, actors, and weeks of post-production. Now, it takes just a couple of files and a click of the "Generate" button.

In this article, we'll explore what Kling 2.6 Motion Control is, how it differs from standard image-to-video models, and how to get the best results for your content.

Welcome to the Era of Controlled AI Video

Kling 2.6 Motion Control is a specialized multimodal model that understands human body physics and cinematic camera movement logic. Simply put, the neural network no longer "guesses" how a character should move. It precisely replicates movement from a reference video and transfers it to your character while fully preserving their appearance.

The result is predictable, visually clean videos suitable for marketing, social media, and production.

What is Kling 2.6 Motion Control?

At its core, Motion Control is based on a simple yet powerful idea:

• You provide a reference image (your character).
• You add a reference motion video (what they are doing).
• The neural network combines them.

Movement, facial expressions, tempo, and weight distribution are taken from the video, while appearance and identity come from the image. Unlike previous image-to-video models, there's minimal AI "improvisation" here. Kling 2.6 acts as a digital "puppeteer," not an inventor.

Key Features of Kling 2.6 Motion Control

Complex Movements and Active Actions

The service confidently handles dancing, fight scenes, and athletic movements. The model understands body inertia and balance. If the reference video features a jump or a sharp kick, the generated character appears heavy and physically plausible, not "clay-like" or obviously AI-generated.

Precise Hand and Finger Movements

Hands are a common weak point in AI video, but this aspect is significantly improved here. Finger and hand motions replicate the real video, which is crucial for gestures, demonstrations, and product scenes.

Scene and Environment Freedom

The background from the reference video is not mandatory. You can change the surroundings using a text description while preserving the character's movement. For example, the character continues walking or dancing but in a different space.

Camera and Perspective Control

Kling 2.6 offers different camera orientation modes. You can define how strictly the AI should follow the camera movements from the video or adhere to the composition of the source image. This provides control over the frame's narrative.

How Motion Control Works in Practice

Simplifying it to a "for dummies" level, the process looks like this:

1. The image tells the neural network who is in the frame.
2. The video shows what they are doing.
3. Kling 2.6 carefully layers one onto the other without breaking anatomy or style.

How to Use Kling 2.6 Motion Control: Step-by-Step

Step 1: Prepare the Source Image

The result's quality directly depends on the image. Pay attention to two key points:

• Visible Limbs. If the image shows hands in pockets but the video features hand-waving, the neural network will have to "imagine" them, often leading to extra fingers or blurred forms.
• Free Space. Leave margin around the edges of the frame. If the character will move their arms widely or dance, they need space within the image.

Step 2: Choose the Motion Video

The reference video is the "skeleton" of the future animation.

The best results come from videos with: one clear character; a simple, contrasting background; and matching scale.

For a talking-head portrait, use a close-up shot. Applying a full-body walking video to a portrait might cause the face to "float" and jerk.

Step 3: Generation

After uploading the image and video, simply click Generate. The output is a ready-made video optimized for TikTok, Instagram, or YouTube. You can download and use it immediately.

Practical Use Cases

Virtual Influencers

Create a brand character and animate it using movements from real people. For example, company employees record videos, and the character replicates their gestures and expressions—no studio or camera required.

Product Demonstrations

Motion Control is excellent for hand-centric scenes: interacting with an interface, gadgets, or physical products. Movements look natural and clear.

Content Localization

Take one high-quality "hero" motion video and apply it to different characters across various age groups, appearances, and ethnicities. The movement remains the same, allowing easy content adaptation for different markets without reshooting.

Conclusion

Kling 2.6 Motion Control isn't just another update; it's a step towards high-quality, controlled video production. This is precisely why we prioritized its integration into the IMI platform as quickly as possible.

If before you had to adjust your plans to fit AI video results, now the results follow your commands. We hope this guide is helpful—and that social media gets flooded with a wave of awesome, viral video content.

Keywords: Kling 2.6 Motion Control, AI video generation, controlled AI video, motion transfer, image to video, video production, AI video editing, virtual influencers, product demonstration AI, IMI platform, AI video tool, character animation AI, AI for marketing.