Introduction

In the era of accessible artificial intelligence, the ability to communicate effectively with neural networks becomes a valuable skill. A well-crafted request (prompt) will help you get exactly what you need from AI, whether it's a detailed text response, a generated image, or even a code snippet. Beginners may find it difficult to write a prompt for a neural network, but anyone can master this skill. In this article, we will explain at a professional level how to write a prompt for a neural network and look at examples of the best prompts. The material will be useful for beginners and everyone who wants to learn how to effectively interact with AI.

What is a prompt and why is it important

A prompt is a text instruction or request that a user gives to a neural network to get the desired result. Simply put, it's a message describing your task. How clearly and thoroughly the prompt is formulated directly affects the accuracy and usefulness of the neural network's response. An incorrectly formulated request often leads to unpredictable or irrelevant results. It's important to remember: the neural network doesn't guess thoughts and relies only on the words and descriptions you provide. If the request is too general, for example, just "draw a forest", the model will produce a maximally averaged result (conditionally, like searching for the word "forest" on stock sites). But if you clarify the details - for example, "coniferous forest in morning fog, in watercolor style" - the output image will be much closer to what you imagined. Thus, a well-formulated prompt serves as a tool for controlling the quality of the AI response.

Good prompt vs. bad prompt

The quality of the request directly affects the accuracy, completeness, and adequacy of the neural network's response. A bad prompt is usually vague and too general, which causes the AI to give random or low-value results. A good prompt, on the contrary, saves your time and helps avoid errors or "hallucinations" of the neural network (when it invents incorrect facts). For example, the request "Write an article about ecology" is formulated too generally - neither the exact context, nor the volume, nor the audience is clear. At best, the bot will start asking clarifying questions, at worst - it will produce useless text. But if you add specifics (what aspect of ecology, for what audience, in what format and volume the article is needed), the result will be much more useful. The conclusion is simple: the best prompts for neural networks are those that are maximally specific and well thought out.

How to write a good prompt: steps and tips

To create a prompt for a neural network, follow this step-by-step algorithm. It's universal for different AI models:

1. Define the goal

Clearly decide what exactly the neural network should do: write text (article, story, answer to a question), generate an image or photo, translate text, write code, etc. Formulate the final result for yourself. If there are several tasks, break them down and ask one request at a time, so that there is no confusion and the model doesn't mix different requirements. For example, first ask the chatbot for a workout plan, and then with the next request - equipment selection, instead of everything at once.

2. Add parameters and constraints

Write down all important details that need to be considered when generating the result. Specify the desired response volume (for example, number of words or minutes for audio/video), the required style or tone (for example, "formal business style" for text or "watercolor drawing" for an image), and the target audience (children, beginners, specialists, etc.). Also immediately include possible constraints: structure requirements (for example, response in the form of a list or table), tone, unwanted content. The more complete the list of conditions, the better the AI will understand the task.

3. Clarify context and requirements

Add information that will help the neural network more accurately implement your idea. It's useful to provide context: the topic or background of the task, source data that the AI should rely on. If necessary, provide examples or samples - text excerpts whose style you want to get, artist names or references for the visual style of the picture. Clearly formulate what not to do: for example, prohibit unwanted details or clarify requirements (don't use jargon in text, "no watermarks" on the picture, etc.). Such clarifications eliminate ambiguity and narrow the field for AI imagination, directing it to the desired result.

4. Check and edit the prompt before sending

Read your prompt and make sure it includes: a) a clear goal, b) sufficient context or description, c) necessary parameters and constraints. Remove vague formulations like "make it beautiful" - instead, describe what exactly is considered a beautiful result for your task. If the model allows, you can structure the request (for example, list "Goal:", "Audience:", "Format:" etc., as in a technical specification). But this is not necessary - the main thing is that all important details are present in the request text. After receiving the response, evaluate whether it matches your original goal. Most often, the result is close to what was intended the first time, but sometimes refinements are needed. Don't be afraid to clarify: instead of completely rewriting the prompt, you can give the neural network additional instructions in the continuation of the dialogue to correct shortcomings. For example, if the chatbot didn't take something into account, you can reply: "Add statistics for 2023 to the text" or "Rephrase the last paragraph in a more friendly tone." In most cases, adjustments within the already received response allow you to "polish" the result to perfection.

Examples of the best prompts for neural networks (for inspiration)

What do well-crafted, successful prompts look like in practice? Let's look at several examples for different tasks - text and graphic.

Example 1 (text neural network)

"You are an experienced editor. Edit the following text for grammar and style, preserving the author's tone. Then explain what corrections you made and why, in list format." - this prompt clearly sets the bot's role ("experienced editor") and formulates a specific task (text proofreading with explanation of corrections), as well as the required response format (list with explanations). Such a request is clear to the AI and will produce a structured, useful result.

Example 2 (image generator)

"Generate an image: a retro car driving on a mountain road against a sunset background. Style - realistic 1980s photography, angle - frontal, lighting - soft warm sunlight. Format - widescreen photo." Here the prompt for the neural network for photos contains a scene description (object - retro car, environment - mountain road, sunset), sets a stylistic context (1980s photography) and technical details - angle and lighting. Even the desired frame format is specified. Such a detailed request significantly increases the chance of getting exactly the image you imagined.

Both examples show signs of a quality prompt: specificity, clear structure, and absence of ambiguities. Beginners will find it useful to study such examples of prompts for neural networks - this gives ideas and understanding of how to formulate their requests.

Features of prompts for different neural networks

Each neural network model may have its own nuances in understanding requests. However, the basic principles (goal, details, constraints) remain valid for any system. Let's look at how to write prompts for the most popular types of neural networks - text chatbots and image generators (neural networks for photos).

Prompts for chatbots and text models

Working with text neural networks (such as ChatGPT, YandexGPT, Google Bard, etc.) is easiest - it's enough to describe the task in ordinary natural language. However, there are several techniques that will help get a better result:

Include everything in one request

In chatbot interfaces, you want to send messages in parts, but it's better to present the entire task as one complete message. This way, the model will get the full context from the start and won't start answering prematurely on half the question.

Set a role or response style

At the beginning of the prompt, you can tell the bot what role it plays. For example: "You are a marketer, an expert in social media. Answer the question..." or "Imagine you are a history teacher...". Setting a role orients the neural network to the desired tone and response style. This is a kind of "command in the prompt for the neural network": a short instruction that strongly influences the nature of the generated text.

Control the course of the dialogue

If the conversation goes wrong - for example, the bot started "hallucinating" and giving incorrect facts - it's better to start a new chat. In the new request, take into account the information that came up earlier, to immediately clarify and avoid repeating the error. For example: "Last time the answer had inaccurate dates, please provide only verified historical facts."

Use ready-made prompts and ideas

It's easy to find the best prompts for neural networks for a wide variety of tasks online - from writing letters to creating marketing plans. Such examples can be taken as a basis and adapted to your situation. This is an excellent way to get prompt ideas. If you found a successful template in English, just paste it into the chatbot and add the phrase "Answer in Russian" - the model will translate the result into Russian. Or ask the neural network itself to adapt the English prompt to the Russian context. Thus, the language barrier won't prevent you from using foreign developments.

Prompts for neural networks for photos (image generators)

Image generators (Stable Diffusion, Midjourney, DALL-E, "Sber Iskusstvo", etc.) perceive requests differently than chatbots. Here the prompt describes a visual scene, and it's important to consider special nuances:

Study built-in settings

Many services have separate fields or commands in the prompt for parameters like image size, aspect ratio, style presets, or degree of match to the request. In Midjourney, for example, the frame format is set with the --ar 16:9 command, and unwanted details can be excluded using --no and a list of words. Start with such settings (through the interface or special words), so as not to duplicate them in text in the descriptive part of the request.

Describe the scene in detail

The neural network doesn't "see" and doesn't imagine the picture - it generates it strictly according to the description. Therefore, instead of abstract phrases about mood, you need to list in detail the objects and background: who or what should be present in the image, where and how it's located. First describe the main objects or characters, then the environment and composition, and only after that can you add general words about mood or atmosphere of the scene.

Specify style, angle, and lighting

Imagine yourself as a director or photographer. Determine in advance what style the image should be in - for example, realistic photo, oil painting, anime style, 3D render, etc. Specify the camera angle (close-up, top view, general scene) and desired lighting (soft diffused light, neon night, etc.) for more specificity. These details help the neural network understand the visual characteristics of the result.

Language of the request

Many popular generators (Midjourney, Stable Diffusion) better recognize prompts written in English. This is because their training datasets are mainly English-language. Therefore, complex requests should be translated into English (you can even use the same chatbot). Exception - neural networks initially trained on Russian (like Kandinsky or "Shedevrum"), for them you can write in Russian right away. If you're not sure which language is better, experiment: often a prompt for a neural network (text) can first be sketched in Russian, then translated.

Photo-realism or drawing?

Let the neural network know what kind of image you want. If you need a realistic picture, add the phrase "a photo of ..." (or in Russian "photography ...") in the description. If an illustration or art is required, you can specify "a drawing of ..." or simply describe the required style (watercolor, pixel art, etc.). A clear indication of format will eliminate ambiguity in style interpretation.

Examples and auto-selection

Don't hesitate to search online for examples of successful prompts specifically for the model you're using. Often the community has already shared ready-made requests that can be taken as a template, substituting your objects and details. In addition, some services have an automatic prompt improvement function - use it if available. The algorithm itself will suggest clarifications or corrections to your description to improve the final image.

Prompts for other neural networks (video, audio, code)

In addition to text and images, there are neural networks for generating video, music, text-to-speech, writing program code, creating presentations, etc. Prompts for them are built on the same principles, but there are some differences:

Video

Here it's especially important to clearly set the script and dynamics: describe camera movement, duration, and scene setting. For example, "Slow panoramic camera movement around a bouquet of flowers on a white background, lighting - studio, duration 10 seconds". Also specify the style (realistic video, animation, etc.). Many video generators allow setting clip length and format through separate interface settings.

Audio and music

In an audio prompt, you need to note the genre, mood, timbre, presence of vocals, language, and theme of the composition. For example: "Sad melody in lo-fi hip-hop genre, no words, slow tempo, for background during study". The more specific the description of the desired sound, the better the result.

Code

If you're using AI to generate code, specify the programming language, desired code function, and any specific requirements in the request (for example, "write a sorting function in Python without using built-in sorting functions"). Clarify what exactly the code should do, and provide examples of input and output data if necessary.

General rule: the more complex or specific the task, the more strictly and structured the prompt should be written. For complex systems that may "get confused" from a long description, try to formulate the request as simply as possible and step by step.

Neural networks for creating prompts (prompt generators)

Interestingly, there are also special tools - essentially neural networks for generating prompts. These are services that translate your ordinary human request into a well-formulated prompt understandable to another model. In other words, they help create a prompt for a neural network, automatically adding details and clarifications. Such prompt generators are especially popular for graphic neural networks, where a very precise and concise descriptive request is required. But there are solutions for chatbots as well.

As a rule, working with them doesn't require registration: you enter a description of the task in your own words, and the service produces an edited version of the request. It's worth noting that the language models themselves (the same chatbots) can also be used for such purposes. You can literally ask the AI: "Write a prompt for [neural network name] so that [desired result]". For example: "Write a prompt for an image generator to get an image of a futuristic city at night in cyberpunk style". Such a meta-approach is often surprisingly effective - using a neural network to create a prompt. Practice shows that ChatGPT and similar models can produce a very detailed and accurate request template that only needs to be slightly adjusted for your needs and copied into the target program. This method often gives an even better prompt than specialized separate services, because you get the result immediately in the context of the dialogue and can immediately ask to improve or clarify it.

Conclusion

How to write the perfect prompt? The main thing is to think structurally and put yourself in the place of the AI, which needs clear and unambiguous instructions. Before sending a request, check against a small checklist:

• Goal is clearly defined - it's clear what should be the output (text, photo, code, music, etc.)
• Context and style are present - topic, tone, audience, response format are specified, or examples are given for reference
• Constraints are written - volume (text size or image), structure (points, lists), unwanted details, quality requirements
• Everything is specific - no vague phrases like "make it beautiful" or "generate something interesting"; instead, specific desired characteristics of the result are described
• Complex task is broken down - if the request is very complex, it's divided into stages or several separate prompts so the model doesn't get confused
• Model features are taken into account - for example, for a chatbot, the desired role and response language are set, for an image generator, the scene and style are described, for video, the script is specified, etc.
• Language is chosen correctly - you can write to a text bot in Russian (or explicitly ask to answer in Russian), but for visual AI it's better to give a description in English for better results

If the prompt satisfies all checklist items, the neural network's response will likely be quality from the first time. Don't forget that perfection comes with practice: experiment, try different formulations. Over time, you'll begin to intuitively feel how to correctly write prompts for neural networks for your tasks, and any collaboration with AI will give the desired and even excellent result. Good luck with your creativity with neural networks!