AI Assistants: Capabilities, Examples, and How to Choose the Best Personal AI Assistant

Swagger is a universal set of tools for designing, documenting, testing, and deploying REST APIs based on the widely accepted OpenAPI and AsyncAPI standards. API vs REST API API (Application Programming Interface) is a set of rules and tools for interaction between different applications. It defines how one program can request data or functionality from another. For example, calling a method from the mathematical module of the Python programming language is the simplest form of a local API, through which different components of a program can exchange data with each other: import math  # importing the math module result = math.sqrt(16)  # calling the API method of the math module to calculate the square root print(f"The square root of 16 is {result}")  # outputting the result to the console In this case, the math module provides a set of functions for working with mathematical operations, and the sqrt() function is part of the interface that hides the internal implementation of the module. REST API is an API that follows the principles of REST (Representational State Transfer) architecture, in which interaction with the resources of another program is performed via HTTP requests (GET, POST, PUT, DELETE) represented as URLs (endpoints). For example, retrieving, sending, deleting, and modifying content on websites on the Internet is done by sending corresponding requests to specific URLs with optional additional parameters if required: Retrieving the main page GET / HTTP/1.1 Host: website.com Retrieving the first page of article listings GET /website.com/page/1 HTTP/1.1 Host: website.com Retrieving the second page of article listings GET /website.com/page/2 HTTP/1.1 Host: website.com Publishing a new article POST /website.com/newarticle HTTP/1.1 Host: website.com Deleting an existing article DELETE /website.com/article/some-article HTTP/1.1 Host: website.com Thus, a REST API is an extension of an API that defines a specific type of interaction between programs. OpenAPI vs AsyncAPI As network interactions developed, the need arose to unify API descriptions to simplify their development and maintenance. Therefore, standards began to emerge for REST and other API types. Currently, two standards are popular. One describes synchronous APIs, and the other describes asynchronous ones: OpenAPI: Intended for REST APIs. Represents synchronous message exchange (GET, POST, PUT, DELETE) over the HTTP protocol. All messages are processed sequentially by the server. For example, an online store that sends product listing pages for each user request AsyncAPI: Intended for event-driven APIs. Represents asynchronous message exchange over protocols such as MQTT, AMQP, WebSockets, STOMP, NATS, SSE, and others. Each individual message is sent by a message broker to a specific handler. For example, a chat application that sends user messages via WebSockets. Despite architectural differences, both standards allow describing application APIs as specifications in either JSON or YAML. Both humans and computers can read such specifications. Here is an example of a simple OpenAPI specification in YAML format: openapi: 3.0.0 info: title: Simple Application description: This is just a simple application version: 1.0.1 servers: - url: http://api.website.com/ description: Main API - url: http://devapi.website.com/ description: Additional API paths: /users: get: summary: List of users description: This is an improvised list of existing users responses: "200": description: List of users in JSON format content: application/json: schema: type: array items: type: string The full specification description is available on the official OpenAPI website. And here is an example of a simple AsyncAPI specification in YAML format: asyncapi: 3.0.0 info: title: 'Simple Application' description: 'This is just a simple application' version: '1.0.1' channels: some: address: 'some' messages: saySomething: payload: type: string pattern: '^Here’s a little phrase: .+$' operations: something: action: 'receive' channel: $ref: '#/channels/some' You can find the full specification description on the official AsyncAPI website. Based on a specification, we can generate various data: documentation, code, tests, etc. In fact, it can have a wide range of applications, to the point where a neural network could generate all the client and server code for an application.   Thus, a specification is a set of formal rules that governs how an interface to an application operates. This is exactly where tools like Swagger come into play. With them, you can manage an API specification visually: edit, visualize, and test it. And most importantly, Swagger allows you to generate and maintain documentation that helps explain how a specific API works to other developers. Swagger Tools Aiming to cover a wide range of API-related tasks, Swagger provides tools with different areas of responsibility: Swagger UI. A browser-based tool for visualizing the specification. It allows sending requests to the API directly from the browser and supports authorization through API keys, OAuth, JWT, and other mechanisms. Swagger Editor. A browser-based tool for editing specifications with real-time visualization. It edits documentation, highlights syntax, performs autocompletion, and validates the API. Swagger Codegen. A command-line tool for generating server and client code based on the specification. It can generate code in JavaScript, Java, Python, Go, TypeScript, Swift, C#, and more. It is also responsible for generating interactive documentation accessible from the browser. Swagger Hub. A cloud-based tool for team collaboration using Swagger UI and Swagger Editor. It stores specifications on cloud servers, performs versioning, and integrates with CI/CD pipelines. Thus, with Swagger, we can visualize, edit, generate, and publish an API.  You can find the full list of the Swagger tools on the official website. Who Needs Swagger and Why So far, we have examined what Swagger is and how it relates to REST APIs.  Now is a good time to discuss who needs it and why. The main users of Swagger are developers, system analysts, and technical writers: the first develop the API, the second analyze it, and the third document it. Accordingly, Swagger can be used for the following tasks: API Development. Visual editing of specifications makes using Swagger so convenient that it alone accelerates API development. Moreover, Swagger can generate client and server code implementing the described API in various programming languages. API Interaction. Swagger assists in API testing, allowing requests with specific parameters to be sent to exact endpoints directly from the browser. API Documentation. Swagger helps form an API description that can later be used to create interactive documentation for API users. That is exactly why we need Swagger and all of its tools: for step-by-step work on an API. How to Use Swagger So, how does Swagger work? As an ecosystem, Swagger performs many functions related to API development and maintenance. Editing a Specification For interactive editing of REST API specifications in Swagger, there is a special tool, Swagger Editor. Swagger Editor interface: the left side contains a text field for editing the specification, while the right side shows real-time visualization of the documentation. You can work on a specification using either the online version of the classic Swagger Editor or the updated Swagger Editor Next. Both editors visualize live documentation in real time based on the written specification, highlighting any detected errors. Through the top panel, you can perform various operations with the specification content, such as saving or importing its file. Swagger Editor Next offers a more informative interface, featuring a code minimap and updated documentation design. Of course, it is preferable to use a local version of the editor. The installation process for Swagger Editor and Swagger Editor Next is described in detail in the official Swagger documentation. Specification Visualization Using the Swagger UI tool, we can visualize a written specification so that any user can observe the structure of an API application. A detailed guide for installing Swagger UI on a local server is available in the Swagger docs. There, you can also test a demo version of the Swagger UI dashboard. A demo page of the Swagger UI panel that visualizes a test specification. It is through Swagger UI that documentation management becomes interactive. Via its graphical interface, a developer can perform API requests as if they were being made by another application. Generating Documentation Based on a specification, the Swagger Codegen tool can generate API documentation as an HTML page. You can download Swagger Codegen from the official Swagger website. An introduction to the data generation process is available in the GitHub repository, while detailed information and examples can be found in the documentation. However, it is also possible to generate a specification based on special annotations in the application’s source code. We can perform automatic parsing of annotations using third-party libraries that vary across programming languages. Among them: Gin-Swagger for Go with the Gin framework Flask-RESTPlus for Python with the Flask framework Swashbuckle for C# and .NET with the ASP.NET Core framework FastAPI for Python It works roughly like this: Library connection. The developer links a specification-generation library to the application’s source code. Creating annotations. In the parts of the code where API request handling (routing) occurs, the developer adds special function calls from the connected library, specifying details about each endpoint: address, parameters, description, etc. Generating the specification. The developer runs the application; the functions containing API information execute, and as a result, a ready YAML or JSON specification file is generated. It can then be passed to one of the Swagger tools, for example, to Swagger UI for documentation visualization. As you can see, a specification file acts as a linking element (mediator) between different documentation tools. This is the power of standardization—a ready specification can be passed to anyone, anywhere, anytime. For example, you can generate a specification in FastAPI, edit it in Swagger Editor, and visualize it in ReDoc. Code Generation Similarly, Swagger Codegen can generate client and server code that implements the logic described in the specification. You can find a full description of the generation capabilities in the official documentation. However, it’s important to understand that generated code is quite primitive and hardly suitable for production use. There are several fundamental reasons for this: No business logic. The generated code does nothing by itself. It’s essentially placeholders, simple request handlers suitable for quick API testing. No real processes. Besides general architecture, generated code lacks lower-level operations such as data validation, logging, error handling, database work, etc. Low security. There are no checks or protections against various attack types. Therefore, generated code is best used either as a starting template to be extended manually later or as a temporary solution for hypothesis testing during development. API Testing Through Swagger UI, you can select a specific address (endpoint) with given parameters to perform a test request to the software server that handles the API. In this case, Swagger UI will visually display HTTP responses, clearly showing error codes, headers, and message bodies. This eliminates the need to write special code snippets to create and process test requests. Swagger Alternatives Swagger is not the only tool for working with APIs. There are several others with similar functionality: Postman. A powerful tool for developing, testing, and documenting REST and SOAP APIs. It allows sending HTTP requests, analyzing responses, automating tests, and generating documentation. Apidog. A modern tool for API development, testing, documentation, and monitoring. It features an extremely beautiful and user-friendly interface in both light and dark themes. ReDoc. A tool for generating interactive documentation accessible from a browser, based on an OpenAPI specification. Apigee. A platform for developing, managing, and monitoring APIs owned by Google and part of the Google Cloud Platform. Mintlify. A full-fledged AI-based platform capable of generating an interactive, stylish documentation website through automatic analysis of a project’s repository code. API tools differ only in implementation nuances and individual features. However, these differences may be significant for particular developers — it all depends on the project. That’s why anyone interested in API documentation should first explore all available platforms. It may turn out that a simple tool with few parameters is suitable for one project, while another requires a complex system with many settings. Conclusion It’s important to understand that Swagger is a full-fledged framework for REST API development. This means that a developer is provided with a set of tools for maintaining an application’s API —from design through documentation for end users. At the same time, beyond classic console tools, Swagger visualizes APIs clearly through the browser, making it suitable even for beginners just starting their development journey. If any Swagger tool does not fit a particular project, it can be replaced with an alternative, since an application’s API is described in standardized OpenAPI or AsyncAPI formats, which are supported by many other tools. You can find Swagger tutorials and comprehensive information about its tools in the official Swagger documentation.

Neural networks and artificial intelligence can process not only text data, videos, and graphics but also work with audio information. This capability makes it possible to create music. Just a few years ago, it was believed that creating your own musical compositions required a studio and instruments, or at least the skills to work with specialized software. However, the rapid growth of artificial intelligence is completely changing this paradigm—now, AI takes on the entire process of creating musical compositions. The user only needs to create a text prompt specifying the requirements for the composition. Today, we review top AI music creation platforms: Suno AI, AIVA, Soundraw, Mubert, MusicGEN, Loudly, Riffusion. How AI Makes Music Before reviewing the AI platforms, let's understand how they make music. Typically, AI uses deep learning to create musical compositions. This method allows analyzing large volumes of musical data and generating new compositions based on it. The algorithm for generating music involves training a model on large datasets (e.g., MIDI files and audio recordings) and then generating music based on parameters such as genre or instruments. Below are the types of neural networks used in music creation: Recurrent Neural Networks (RNN) A recurrent neural network is a deep learning model trained to process and transform sequential sets of input data into sequential output. Sequential data are data in which components have a strict order and relationships based on complex semantics and syntactic rules, such as words and sentences. As mentioned earlier, RNNs are well-suited for working with sequences. In music, these sequences are melodies and chords, thanks to the network’s ability to "remember" previous notes. Transformers Transformers are a type of neural network architecture designed to transform an input sequence into an output sequence. They study context and track relationships between components of a sequence. In music creation, transformers are used to handle complex musical structures and generate multilayered compositions. Generative Adversarial Networks (GAN) GANs are named for their use of two neural networks that "compete" with each other: one network generates data samples, while the other tries to predict whether the data is original. In music generation, one network creates tracks while the other evaluates their quality, improving the final result as needed. Autoencoders Autoencoders are neural networks that do not use supervision during training and do not rely on data compression. They are used to create variations based on existing tracks or to apply musical stylization. Suno AI Suno AI is a popular AI music software launched in December 2023 that creates vocal and instrumental tracks using a simple text prompt. You can specify the style of the composition and the song lyrics in the prompt. Its popularity led Suno, Inc., in partnership with Microsoft, to integrate Suno AI into the Microsoft Copilot chatbot. Suno AI is ideal for background music and advertising tracks. Advantages: Simple and user-friendly web interface. Supports using images and videos in addition to text prompts. Completely ad-free in the free version. Provides editing tools for generated tracks. Automatic selection of cover images for compositions. Official mobile app available for iOS and Android. Disadvantages: The free version includes 50 credits, allowing only 5 compositions per day; 50 more credits are added daily. Duration limits depend on the AI model used: v2 up to 1:20 min, v3 up to 2 min, v3.5 up to 4 min. AIVA AIVA is one of the best AI music generators designed specifically for creating music, from classical and symphonic compositions to electronic dance music tracks. AIVA was first released in February 2016 by Luxembourg-based Aiva Technologies SARL. Advantages: Advanced editing tools: change tempo, key, duration, style, and instruments. Ability to upload existing tracks to use as templates. Export of compositions in MIDI, WAV, or MP3. Official documentation available. Available as a web interface or desktop app (Windows, macOS, Linux). Monetization of tracks (only in the Pro plan). Disadvantages: The free plan allows only 3 downloads per month. Limited editing features in the free version. Soundraw Soundraw is an online AI song generator, launched in February 2020 by Japanese company SOUNDRAW, Inc. Soundraw is suitable for creating tracks in any genre. It can be used by individuals to create personal tracks or by artists and labels for commercial music (paid plans only). Advantages: Simple, intuitive web interface. Ability to mix multiple genres in a track. Extensive editing options: track length, tempo, genre, mood (epic, happy, angry, sentimental, romantic, etc.), and theme (corporate, cinematic, comedy, documentary, etc.). API available (as of 2025, API for music generation is available in the Enterprise plan only). Disadvantages: Track downloads require a subscription. Mubert Mubert is an online AI platform for generating music tracks in real-time using text prompts, images (.png, .jpg, .webp), or by selecting a genre. Ideal for background music in videos and podcasts. Advantages: Simple 3-click track creation. You can specify genre, mood, track type (Track, Loop, Mix, Jungle), and duration (5 seconds–25 minutes). API available (beta) for registered users. Mubert Studio allows monetization and promotion of tracks. Official iOS and Android apps available. Integration with YouTube, Twitch, TikTok, Streamlabs, Kick. Disadvantages: Instrumental-only tracks; no vocals. Free plan: 30 min/day, 25 tracks/month; paid plans increase limits (up to 500–1000 tracks). Cannot mix multiple genres or use sound effects. No track stems or MIDI export. MusicGEN MusicGEN is a simple AI service for creating music via text prompts or audio samples. Focused on short tracks (up to 2 minutes). Requires installation and setup, which can be challenging for beginners. Advantages: Simple interface. Open-source AudioCraft language model used in MusicGEN and AudioGen. Ready-made implementations available online. Disadvantages: Requires technical skills for setup. Tracks limited to 15 seconds. No customization during track creation. Loudly Loudly is a platform with built-in AI for generating music and tracks. Tracks can be created via text description or a built-in generator. Ideal for social media, videos, and streaming services. Advantages: Rich functionality: choose instruments, genre (15+ including EDM, Hip Hop, Techno, Rock), tempo, subgenres. Built-in templates with flexible filters. API available on request. Disadvantages: Free version: 25 tracks/month, 30 sec each; cannot download tracks. Riffusion Riffusion is an AI service based on the Stable Diffusion deep learning model, generating short music fragments including vocals using text prompts. Advantages: Free, unlimited creation in "relax mode." Ability to create remixes and covers. You can provide the song lyrics.  The web version allows grouping tracks into projects and playlists. Disadvantages: Paid plan required for commercial use. Paid plans allow audio uploads, WAV and Stem downloads. Limited editing functionality compared to competitors. Conclusion: Comparative Table Feature Suno AI AIVA Soundraw Mubert MusicGEN Loudly Riffusion Music creation method Text, images, video Styles, chords, MIDI, or track Interface with options Text, images, filters (genre, mood, tempo) Text prompt, audio import Text prompt, generator Text, image, interface with options Free plan Limited: 5 compositions/day (50 credits) Limited: 3 tracks/month, max 3 min, MP3/MIDI only Limited: cannot download Limited: 25 tracks/month, MP3 only Unlimited Limited: 25 tracks/month, max 30 sec, no download Limited: cannot download or use commercially Paid plans Pro $10, Premier $30/month, 20% annual discount Standard €15, Pro €49/month, 33% annual discount $11.04–$32.49/month, Enterprise by request $11.69–$149.29/month, custom & lifetime plans None (open-source) Personal $10, Pro $30/month Starter $8, Member $48/month, 25% annual discount Interface language English English English, Japanese English, Spanish, Korean English English English Supported song languages 50+ English English English English English English Music editing Text, style, audio template, instrumental style, duration Tempo, chords, instruments, effects, duration Tempo, genre, mood, theme, duration Genre, mood, track type, duration (5 sec–25 min) None Genre, mood, tempo, instruments, duration Text, style Commercial use Paid plans only Pro plan only Artist Starter & above Paid plans only None Paid plans only Paid plans only API No No Yes Yes (on request) No Yes No Export formats Free: MP3, Paid: MP3, WAV, stems Free: MP3, MIDI; Pro: MP3, WAV Paid only: MP3, WAV, stems Free: MP3 (25 tracks/month), Paid: up to 1000 tracks WAV only Paid: MP3, WAV Paid: WAV, stems Mobile app Yes (iOS, Android) No No No No Yes (iOS, Android) No Desktop app No Yes (Windows, macOS, Linux) No No No No No

ChatGPT is a powerful tool for generating text, code, creating content strategies, and even working with images. It allows users to get accurate answers to complex questions across many fields of human activity. Developed by OpenAI based on the GPT architecture, this AI assistant can understand context, maintain long conversations, and adapt its communication style to the user’s needs. To get the most useful answers, it’s important to learn how to properly formulate requests, or “prompts,” for ChatGPT. We’ll explain this in more detail below. We’ll also share the best ChatGPT prompts to help you work more efficiently, show how to build well-structured requests, give examples with detailed ChatGPT responses, and explain how to write prompts for visual content through DALL·E. All examples in this article use the free version of ChatGPT. What Prompts Are and Why They Matter A prompt is a text request that a user sends to ChatGPT or another AI model to get a desired answer. Simply put, it’s an instruction for the neural network explaining exactly what you want to receive. Why is it important to create a good prompt for ChatGPT? Here are the main reasons: Improved answer accuracy: The clearer and more detailed your prompt, the more relevant and useful the response will be. Time savings: A well-structured request saves you from repeatedly rephrasing or clarifying your question. Fewer mistakes: Clear instructions reduce the risk of incorrect or outdated information. Optimized workflow: Good prompts let you automate complex tasks, from content creation to data analysis. Structured results: Properly designed prompts help get answers in the needed format: lists, tables, step-by-step guides, etc. Personalized responses: Adding context to your request makes ChatGPT’s answers more relevant to your needs (context includes role, tone, audience, format, etc.). Better AI learning: Well-crafted prompts help the AI understand your preferences over time. That’s why it’s best to keep an ongoing conversation with ChatGPT within one chat thread when working on the same topic. ChatGPT analyzes your request and provides the most relevant answer based on previously learned data. The clearer your prompt, the more accurate the AI’s response will be. Examples of Weak and Strong Prompts 🔴 Weak Prompt 🟢 Strong Prompt “Collect information about clouds.” “Write a 1,000-word piece about the benefits of cloud technologies for small businesses. Include a comparison of Hostman with competitors.” “Tell me about hosting.” “Compare Hostman and AWS pricing for high-traffic websites. Highlight the pros and cons of each.” “Write something about marketing.” “Write 5 marketing strategies for promoting a SaaS product in 2025 via Facebook. Format: short description + 3 concrete actions for each.” How to Create Your Own Prompts Below are the main rules for writing effective prompts and common mistakes to avoid when working with ChatGPT. Rules for Writing Prompts Main principles for crafting perfect prompts: The more specific your request, the more relevant the answer. Always specify the desired output format (list, table, step-by-step guide). For professional tasks, add context (AI’s role, difficulty level, target audience). Use examples and analogies to match your expectations precisely. Clearly state any constraints or special requirements. Indicate timeframes for data relevance. Ask for sources when you need verified information. Balance detail with conciseness. Another useful tip: save successful prompts somewhere convenient: a text editor, personal notes, or a dedicated ChatGPT chat named “Templates.” This helps in the future since many prompts can be reused simply by changing key parameters. You can also use existing prompt libraries and adapt them to your needs, for example, prompthackers.co. Common Mistakes Here are typical mistakes when writing prompts, along with examples of how to improve them: Too general requests 🔴 “Tell me about AI.” 🟢 “Explain how ChatGPT is used in the banking sector in 2025.” Lack of structure 🔴 “Give tips on time management.” 🟢 “Create a checklist: ‘5 time management methods for remote workers.’ Format: Name → Essence → Example.” Ignoring context 🔴 “Write a text.” 🟢 “Write a commercial proposal for Hostman (audience: CTOs of mid-sized companies). Tone: expert, but conversational.” Vague clarifications 🔴 “Make it shorter.” 🟢 “Reduce to 300 words, keeping key data from the table.” Overloading with details 🔴 “Write an article about cloud technologies but exclude AWS, Microsoft Azure, IBM Cloud, Oracle Cloud, DigitalOcean, Linode, Vultr.” 🟢 “Write an article ‘AWS Alternatives for Small Businesses’ with the main focus on Hostman” Top 10 Universal Prompts for ChatGPT This section includes ready-made prompt templates that will become reliable tools when working with ChatGPT. These prompts cover a wide range of tasks, from creative brainstorming to complex technical analysis. We’ll look at 10 universal and practical prompts for the following categories: Analysis and comparison Idea generation Psychology and self-development Content strategy Writing and editing Programming Image generation (DALL·E) Learning and education Business Creativity Each template is: Well thought out: structured for high-quality answers Universal: suitable for both beginners and professionals Flexible: easily adaptable to specific needs To use a template, choose the category and replace the placeholders in square brackets with your own values. For complex tasks, you can even combine several templates into one (an example will be shown at the end of the section). All prompts are optimized for GPT-4 and newer versions to ensure highly relevant results even for advanced professional use. 1. For Analysis and Comparison Purpose: Professional comparison of products, services, or technologies based on specific criteria with expert conclusions. Ideal for: Selecting IT solutions, preparing reviews, making business decisions. Template: Compare [Object A] and [Object B] by the following criteria: [1–5 parameters]. Format: table with columns “Feature,” “Object A,” “Object B,” and “Recommendation.” Specify the best option for [scenario]. Example: Compare Hostman VPS and Linode VPS by: price per 1 vCPU, SLA, support speed, and control panel usability. Highlight the optimal choice for a startup with 50K visitors/month. ChatGPT response: Tips: Set timeframes: 🔴 “Compare hosting prices.” 🟢 “Compare 2025 hosting prices including seasonal discounts.” Ask for data sources: 🔴 “Which platform is better?” 🟢 “Compare using data from official websites and independent tests.” Provide context: 🔴 “Which is cheaper?” 🟢 “Which is more cost-effective for a site with 50K visits/month: shared hosting or VPS?” Ask for alternatives: “If the budget is limited to $35/month, what are Hostman’s alternatives?” Specify output format: “Present the data in a table, then give a short verdict for beginners.” 2. For Idea Generation Purpose: Structured brainstorming with clear logic. Application: Finding concepts for startups, content marketing, product design, or creative projects. Template: As a [role], suggest [N] ideas for [task]. Structure: 1) Title → 2) Target Audience → 3) Benefit → 4) Example → 5) Risks.Focus on: [requirements]. Example: As an art director, suggest 5 ad campaign ideas for Hostman in the metaverse. Focus on interactivity and B2B audience. ChatGPT response: Tips: Rank ideas by priority: 🔴 “Give 5 post ideas.” 🟢 “Suggest 5 social media post ideas about Hostman, sorted by feasibility/effectiveness. Consider: budget up to $100, B2B engagement.” Define evaluation criteria: “Exclude ideas requiring more than 3 days to execute.” “Prioritize ideas with viral potential.” Ask for examples: “Show similar cases from the industry for the top 3 ideas.” Limit scope: “Only ideas that don’t require contractors.” “Focus on formats: guides, case studies, interactives, polls.” Request next steps: “For the best idea, outline a 3-day action plan.” 3. For Psychology and Self-Development Purpose: Scientifically grounded methods for solving personal and professional issues. Especially useful for: coaching, stress self-help, and developing emotional intelligence. Template: As a [specialist], create a [duration]-long plan for solving [problem].  Include: 1) Theoretical foundation → 2) Step-by-step techniques → 3) Self-diagnosis tools → 4) Recommended resources.  Adapted for: [audience]. Example: As an HR expert with experience in IT, design an 8-week onboarding program for a new employee at a cloud company. Include: Role introduction plan (days 1–30, broken down by week) Methods for evaluating professional skills (checklists, test tasks) Mentorship system (roles, meeting frequency, KPIs) Recommendations for integrating into corporate culture (events, company traditions) ChatGPT response: Tips: Require scientific backing: 🔴 “How to deal with anxiety?” 🟢 “Using CBT (Beck) and the ABCDE model (Ellis), propose a 4-week anxiety management plan for IT specialists. Include research on the effectiveness of these approaches.” Specify theories: “Explain burnout stages using the Maslach model (emotional exhaustion → cynicism → reduced productivity).” “For procrastination, use Piers Steel’s temporal motivation theory.” Request context adaptation: “Apply Gestalt therapy techniques to conflict situations in remote teams.” “How can the GROW model be applied to IT career coaching?” Ask for self-assessment tools: “Add a checklist for tracking progress on a 1–10 scale.” “What 3 questions can help identify the stage of stress according to Selye?” Limit complexity: “Explain terms in simple words, suitable for beginners.” “Exclude medical recommendations.” 4. For Content Strategy Purpose: Comprehensive publication planning with measurable KPIs. Ideal for: blogging, SMM, email marketing, and sales funnels. Template: As a [position], create a [time period] strategy.  Include: 1) Target personas → 2) Thematic clusters → 3) Calendar (format/KPIs) → 4) Tools. Example:  As a head of content marketing, develop a quarterly blog strategy for Hostman with KPIs focused on trial conversions. Emphasize guides about migrating from competitors. ChatGPT response: Tips: Tie it to business goals: 🔴 “Need a content plan.” 🟢 “Develop a quarterly strategy for the Hostman blog with KPI: +15% increase in trial conversions. 70% educational content, 30% case studies.” Specify success metrics: “For Facebook posts, define target metrics: CTR >3%, engagement >5%.” “Estimate potential reach for each topic.” Request cross-channel integration: “How can a guide be turned into a Facebook post series and email campaign?” “Propose a cross-promotion scheme between YouTube and the blog.” Ask for competitor analysis: “Add analysis of 2 successful strategies from competitors in the cloud segment.” “Which topics bring the highest engagement for competitors?” Limit resources: “For a 2-person team: 1 long-read per week + 3 social channels.” “Without hiring copywriters.” 5. For Working with Texts Purpose: Creating and optimizing commercial or informational materials. Fields: copywriting, SEO, technical documentation, scripts. Template: As a [role], write a [type of text] for [target audience]. Parameters: length → style → required elements → restrictions → SEO. Structure: [sections]. Example: As a technical writer, create a guide titled “Setting Up WordPress on Hostman” (1,500 words). Avoid jargon, include GIF instructions. ChatGPT response: Tips: Clearly define the text’s purpose: 🔴 “Write a text about clouds.” 🟢 “Write a commercial proposal for Hostman aimed at small businesses. Goal: conversion into demo requests.” Set style and tone: “Tone: friendly yet professional, as if explaining to a colleague.” “Avoid bureaucratic phrases; write naturally.” Add SEO parameters if needed: “Include keywords: ‘cloud hosting,’ ‘VPS for business,’ ‘reliable hosting.’ Keep keyword density natural.” “Add LSI words: ‘scalability,’ ‘data security,’ ‘uptime.’” Request examples and comparisons: “Provide 3 strong headline examples for this kind of article.” “Compare with competitor texts: what can be improved?” Limit length and complexity: “Max 1,000 words, divided into H2-H3 subheadings.” “Explain terms like ‘CDN’ in parentheses in simple words.” 6. For Programmers Purpose: Code generation and analysis with full documentation. Main uses: writing scripts, debugging, creating APIs, automating DevOps processes. Template: As a [language] developer of [X] level, write code for [task]. Input → expected output → constraints → requirements. Format: algorithm → code → tests. Example: As a senior Python developer, create a server monitoring script for Hostman with API integration and Telegram notifications. Requirements: async, logging. ChatGPT response: Tips: Specify exact versions: 🔴 “Write a backup script.” 🟢 “Write a Python (3.10+) script for daily MySQL (8.0) backups to Hostman S3. Requirements: async, file logging, Telegram error alerts.” Request explanations: “Add comments every 5 lines to clarify complex code sections.” “Explain why you chose this algorithm (e.g., QuickSort vs. MergeSort).” Require tests: “Add 3 unit tests with edge cases.” “How to test this API in Postman?” Ask for alternatives: “Show alternative solutions in Go and Rust. Compare performance.” Set constraints: “No external libraries.” “Execution time ≤100ms for 10K records.” 7. For Image Creation (DALL·E) Purpose: Precise technical specifications for neural image generation (DALL·E). Applications: ad banners, article illustrations, concept art, presentations. Template: As an art director, create a prompt: 1) Object → 2) Style → 3) Composition → 4) Color palette → 5) Lighting → 6) Restrictions. Goal: [usage]. Example: Create a prompt for a “Hostman Enterprise” banner: a cyberpunk-style server, palette #0A1640/#00C1FF, HUD elements, no people. ChatGPT response: Image generated by ChatGPT: Tips: Be extremely specific: 🔴 “Draw a cloud server.” 🟢 “Generate a 3D render of a Hostman server in blue-white tones. Style: cyberpunk with neon accents. Background: network map with nodes. Aspect ratio 16:9, no people.” Reference known styles: “In the style of the interfaces from the Foundation series.” “Like Wired magazine covers from the 2020s.” Control composition: “Main object centered, occupying 70% of the frame.” “Blurred background with depth-of-field effect.” Request variations: “Show 3 versions: minimalism, retro-futurism, and photorealism.” “Change only the palette to dark/light mode.” Technical constraints: “No text in the image.” “Resolution: 1024×1024, format: PNG.” 8. For Learning and Education Purpose: Designing educational programs using modern methodologies. Application: course creation, training materials, workshops, interactive modules. Template: As a professor of [subject], design a [number]-hour module. Include: goals → plan (theory/practice) → adaptations → glossary. Constraints: [parameters]. Example: Develop an 8-hour course “Cloud Fundamentals” for university students: lectures in Prezi, labs on Hostman, quizzes in Kahoot. ChatGPT response: Tips: Base on teaching models: 🔴 “Create a Python course.” 🟢 “Using the ADDIE model (Analysis, Design, Development, Implementation, Evaluation), create a 4-week course ‘Python for Data Analysis.’ Goal: teach students to visualize data using Matplotlib.” Define difficulty level: “For junior DevOps: basics of Kubernetes.” “For senior developers: algorithm optimization in C++.” Add interactive elements: “Include 3 simulated real-world cloud development cases.” “Propose a gamification format for a cybersecurity module.” Require practical tasks: “Design a lab exercise: deploying a web app on Hostman.” “Create a test assignment with automatic checking via GitHub Actions.” Consider technical limitations: “Course must run on low-end PCs (no Docker).” “Use only free tools (VS Code, Colab).” 9. For Business Purpose: Strategic market and process analysis. Applications: business planning, SWOT analysis, competitor research, financial modeling. Template: As a consultant from [company], conduct an analysis of [object] using the following framework:  1) Market Size → 2) PESTEL → 3) Benchmarking → 4) SWOT → 5) Forecasts. Data sources: [list of references]. Example: Analyze the European cloud gaming market: 2024 market size, PESTEL factors, comparison of NVIDIA GeForce Now / Shadow PC / Boosteroid, and projections through 2026. ChatGPT response: Tips: Be specific with goals: 🔴 “How to increase profits?” 🟢 “Develop 3 revenue growth strategies to increase a SaaS startup’s revenue by 30% within 6 months. Focus: upselling existing clients and reducing churn rate. Use the AARRR framework.” Ask for supporting data: “Analyze the European cloud services market (size, trends, competitors). Use sources such as Statista, Gartner, and official company reports.” “Calculate CAC for our current ad campaign.” Request alternative approaches: “What are the best options for entering the EU market: partnerships vs. independent launch?” “Compare investment risks for expanding VPS services versus cloud storage solutions.” Link to business processes: “How can the new product be integrated into our existing SaaS ecosystem?” Consider resource limitations: “Budget: up to €50,000, team of 5 people.” “Propose solutions that don’t require hiring additional staff.” 10. For Creativity Purpose: To generate compelling stories and concepts. Ideal for: Writers: for books and short stories; Screenwriters: for films and series; Game developers: for characters and worldbuilding; Musicians: for album or concept creation. Template: As a [profession], create a [type of work] in the [genre] style. Parameters: Characters → Setting → Conflict → Style. Format: Logline → Synopsis → Scene breakdown. Example: As a Black Mirror-style screenwriter, develop a concept for an episode about AI in 2045, exploring the theme “Privacy vs Convenience.” ChatGPT response: Tips: Be specific about genre and audience: 🔴 “Write a story about a scientist.” 🟢 “Write the first chapter of a science fiction story about a bioengineer who discovers how to edit DNA using quantum computers. Style: mix of Black Mirror and The Martian. Audience: hard sci-fi fans (ages 25–45), with emphasis on scientific realism.” Request structure: “Outline the plot using Joseph Campbell’s ‘Hero’s Journey’ model.” “Create a dialogue example with subtext (in the style of Aaron Sorkin).” Ask for visualization: “Describe a key cinematic shot for a poster in the style of Blade Runner.” “Which color palette best conveys the atmosphere?” Avoid clichés: “Exclude tropes like ‘the chosen one’ or ‘evil AI.’” “Suggest three unexpected plot twists.” Consider technical constraints: “Script for a 10-minute short film (maximum 5 locations).” “Concept for a mobile game with simple gameplay.” Combined Prompt Example The prompt templates presented above cover most professional user tasks. For maximum efficiency, you can combine them, for example: analysis (section 1) + text generation (section 5) + visualization (section 7). Example prompt Act as both an IT analyst and a digital marketer. I need a comprehensive comparison of cloud hosting platforms (AWS, Google Cloud, and Hostman) with materials ready for publication. Perform the following tasks sequentially: 1. Conduct a detailed analysis: Compare by: cost per vCPU, SSD size, network bandwidth, SLA uptime. Present results in a table with columns: “Feature,” “AWS,” “Google Cloud,” “Hostman.” Conclude with a recommendation for a startup with a $50/month budget. 2. Write an SEO article based on the analysis: Title: “AWS vs Google Cloud vs Hostman: An Objective Comparison for 2025.” Length: 2,000 words. Structure: Introduction (importance of choosing the right provider); Methodology; In-depth review of each provider; Summary table (from step 1); Recommendations for different use cases. Tone: Expert but accessible; Keywords: “cloud hosting,” “VPS comparison,” “Hostman review.” 3. Create visualization prompts (for DALL·E or Midjourney): Style: Corporate infographic (blue and white color palette). Elements: 3D servers with provider logos; Comparative performance and pricing charts; “Price/Performance” scale; Minimalist background with digital accents. Formats: Article cover, comparative infographic, architecture diagram. 4. Additional tasks: Suggest 3 social media posts based on the article. Format: “Did you know that…” + key takeaway + infographic. Platforms: LinkedIn, Reddit. Ensure all data is consistent across text and visuals. Numbers in the text must match tables and charts. Use professional terminology, but explain complex terms for beginners. ChatGPT response: This prompt structure provides: A unified request instead of multiple separate ones; Logical flow: analysis → writing → visuals → promotion; Consistent data across all materials; Publication-ready results. For even higher precision, you can add: “Before starting, ask 3 clarifying questions to better define the task.” This approach helps the AI better understand the project and deliver higher-quality results. Key Takeaways In this article, we explored what prompts are and how to craft them effectively, showcasing 10 universal examples across different categories. A prompt is a text instruction you send to ChatGPT to get a desired response. The clearer and more detailed the prompt, the more accurate and useful the result. Core principles of effective prompting: Clarity and detail (including timeframes, parameters, and constraints); Specify the response format (table, list, step-by-step guide); Add context (AI role, complexity level, target audience); Include examples and analogies for clarity; Note technical requirements (length, tone, restricted elements). Common mistakes: Overly vague prompts (“Write something”); No structure or logic; Ignoring context (missing role or audience); Overcomplicating with conflicting details; Poor clarification (missing data or specific names). Improvement tips: Start broad, then refine details step by step; Save successful prompts as templates; Request data sources for analytical tasks; Use iterations: “Add to the previous answer…” Additional recommendations: For creative work, include stylistic references; For technical tasks, specify software versions or languages; For business analysis, ask for alternative scenarios; Always verify critical data. ChatGPT is a tool, not a substitute for expertise. Save the templates from this guide as a quick-reference list and adapt them over time to fit your workflow. By mastering the art of crafting effective prompts, you’ll unlock ChatGPT’s full potential, transforming it into a personal assistant for work, creativity, and learning. Experiment with phrasing, analyze results, and refine your prompts: that’s how you’ll make AI a truly powerful tool in your toolkit.