Best prompt for a neural network: how to formulate a request and get an accurate result

The best prompt for a neural network is not a universal command, but a request with a clear task, context, constraints, and response format. The more precise the input data and result criteria, the less ChatGPT, Claude, or another model has to fill in on its own.

The request “write an article” often produces generic text, “take a photo” leads to a random composition, and the request “analyze the data” ends with general conclusions. A neural network does not know the goal, audience, source material, or acceptable boundaries until the user specifies them in the prompt.

If a paid model is needed for the task—for example, GPT-5.6 Terra—it is cheaper to arrange access through the partner service Clodex rather than directly from the vendor. The price difference is shown below.

The essentials

• The best prompt starts with a specific task, not a collection of “magic” words.
• Text-based neural networks and image-generation services require different kinds of prompts.
• A role, context, source data, constraints, and response format make the result controllable.
• Ready-made prompts need to be adapted to the product, audience, publication channel, and work objective.
• Figures, dates, studies, names, and links in an AI-generated response require verification against primary data.

What is a prompt, and why does the result depend on it?

A prompt is a request or instruction that a neural network uses to generate text, analysis, an image, a script, a table, or another result. For the model, the prompt defines the boundaries of the task: what should be considered source data, what kind of answer is needed, and which actions are not allowed.

A short request saves time only at the start. The phrase “write a post about a product” does not say who the text is addressed to, what problem the product solves, which facts have been confirmed, what tone the brand needs, or what the reader should do after publication. In response, the model fills the gaps with assumptions.

Working effectively with a neural network is similar to assigning a task to an editor or analyst. The clearer the input, the easier it is to evaluate the result and specify exactly what needs to be corrected. In Razum’s SEO materials on neural networks we examine such scenarios for content, SEO tasks, and workflows.

What the best prompt looks like: a universal formula

There is no universal phrase for every task. However, there is a sequence that helps you properly create prompts for text, analysis, ideas, and image generation.

1. Role or operating mode. Specify the role in which the neural network should respond: editor, analyst, marketer, teacher, or designer.
2. Task. Formulate the action: prepare an article outline, compare options, write an email, identify risks, or describe an image.
3. Context. Provide information about the product, audience, topic, platform, objective, and available source materials.
4. Constraints. List prohibitions: do not invent facts, do not use bureaucratic language, do not write a long introduction, and do not add emotional pressure.
5. Output format. Specify the form: table, outline, list of key points, email, text with subheadings, or several options.
6. Quality criteria. Explain what you are checking: accuracy, clarity, consistency with the tone, absence of repetition, reasoning, and completeness.
7. Clarification process. Ask questions before completing the task if the input data is insufficient.

A ready-made template could sound like this: “Work as an editor. Prepare an article outline for online store owners about the causes of declining organic traffic. The goal of the text is to explain the diagnostic process without unsubstantiated promises. Use only the key points from my materials, and mark unknown data as requiring verification. Provide an outline with H2 headings, a brief description of each section, and questions that need to be clarified before writing the draft.”

Length alone does not make a request stronger. Useful specificity is more important than dozens of contradictory requirements.

An example of a good and a weak request

Weak request

“Write an article about business promotion.”

The neural network does not know what type of business is being discussed, who the text is for, or what should count as promotion. It receives no length, structure, source material, constraints, or quality criteria. The result usually looks like a general set of recommendations that requires substantial reworking.

Improved request

“Prepare a draft article for an internet marketing blog. Audience: owners of small online stores who manage their website and advertising themselves. Topic: how to determine why pages are not receiving search traffic. Goal: provide a clear initial-check process without promising improved rankings. Use my key points about technical indexing, semantics, and page quality. Structure: a short lead, five sections with H2 headings, and an action checklist at the end. Write in Russian, without bureaucratic language or invented statistical data. If a key point requires primary data or expert verification, mark this on a separate line.”

This prompt contains the task, audience, goal, source points, format, and constraints. The model can still make mistakes, but the editor immediately understands the criteria for further refinement. Read more about combining AI and search promotion in the overview of tools for accessing ChatGPT and AI for SEO tasks.

Ready-made prompts for popular tasks

These ready-made prompts serve as a starting point, not as commands to copy without changes. Before running one, replace the general information with data about your own task, audience, and materials.

The best prompt for ChatGPT: text and work documents

“Work as an editor. Prepare the first draft of an instruction for employees on the topic I will describe below. First, ask questions if source data is missing. Do not invent facts, rules, figures, or document titles. Separate confirmed information from assumptions. Format the result with a heading, logical sections, an action list, and a block listing items the author should verify.”

A prompt for ChatGPT is suitable for an email, instruction, article outline, service description, or editorial revision. A precise response format helps avoid wasting time reconstructing the text later.

Prompt for analyzing information

“Analyze the materials I provide. First, briefly list what data you received and what is missing for drawing a conclusion. Then compare the options according to the specified criteria and identify risks and contradictions. Do not add information that is not present in the source files. Present the conclusion in a table: criterion, observation, basis in the material, and question for additional verification.”

A neural network can speed up the analysis of a table, document, or set of notes. Its conclusions should not be treated as a finished expert opinion: compare every significant conclusion with the source data.

Prompt for ideas and a content plan

“Suggest topics for a content plan. Audience: specialists and entrepreneurs who need to solve the stated problem. Product or topic: my source data is below. Platform: a blog or social network. For each idea, specify a headline, the audience’s search or practical question, the material’s brief benefit, and the recommended format. Do not repeat topics using different wording or promise a guaranteed result.”

A list of topics becomes more useful when each idea is tied to a real reader question. For SEO content, this is not enough: topics should be compared with the semantic field, current website pages, and user intent.

Prompt for an image and an AI photo shoot

“Create a photorealistic image of a specialist at a desk. Scene: a bright office, with a laptop and paper notebook on the desk. Pose: the person is looking at the screen, with their hands naturally visible in the frame. Lighting: soft daylight from a window. Camera angle: medium shot at eye level. Style: business editorial photography without an overtly staged advertising look. Do not add readable text, logos, extra fingers, distorted facial features, or unrelated objects.”

A prompt for a photo is best structured from the main subject to the scene, lighting, camera angle, style, and constraints. Midjourney, Stable Diffusion, and other tools interpret wording differently, so one request cannot be considered equally suitable for every system.

Prompt for animating a photo

“Animate the source photograph with a natural, short movement. The person slightly turns their head toward the camera, blinks calmly, and maintains a neutral expression. The camera moves smoothly, while the background remains stable. Do not change the face, hairstyle, clothing, body proportions, or objects in the background.”

A prompt for animating a photo should describe the action, direction of movement, facial expression, camera, and prohibitions against distortion. Quality depends on the source image and the capabilities of the chosen tool.

Prompt for learning and working with materials

“Help prepare a plan for independent research on the topic I will specify. Explain key terms in simple language, suggest research questions, show a possible line of reasoning, and list the types of sources to use for verification. Do not write the finished work instead of the author or invent a bibliography.”

A neural network helps explore a complex topic, build a structure, and identify weak points in an argument. Responsibility for independent work, source reliability, and compliance with the educational institution’s rules remains with the author.

If you decide to take out a paid plan while reading, compare the official price with the price through a partner before subscribing directly: the difference is usually several times, and the calculation is provided at the beginning and end of the article.

How to create prompts for different neural networks

Text-based neural networks

ChatGPT, Claude, and other text models work well with a defined task, context, examples of the desired style, constraints, and response format. A complex request is best divided into stages: first obtain an outline, then expand the sections, then edit the wording, and separately check factual claims.

But what should you do if the model confidently answers a question without showing its basis? Ask it to separate facts from assumptions, list its premises, and specify which source data it used. This does not replace verification, but it makes the weak points in the response more visible.

Neural networks for image generation

Image generation requires a detailed description of the scene. The main subject, action, surroundings, composition, lighting, camera angle, style, materials, color palette, and constraints usually influence the result more than abstract requests to “make it beautiful.”

Russian is suitable for getting started. Some models may interpret Russian and English prompts differently, so if the result is ambiguous, try testing a more precise formulation or translation while preserving the meaning and constraints.

Why a neural network does not understand the task: 5 common mistakes

The request is too general. Reason: the user names only the topic. Consequence: the model produces a generic answer. Correct action: add the goal, audience, expected result, and format.

No source data or context. Reason: the neural network receives no source material about the product, document, or situation. Consequence: guesses and generic statements appear in the text. Correct action: provide key points, specifications, constraints, and confirmed materials.

The response format is not specified. Reason: the model chooses the structure itself. Consequence: an essay arrives instead of a table, or a long text appears instead of an outline. Correct action: immediately specify the required form and the composition of the sections.

The quality criteria are vague. Reason: the request to “make it beautiful” does not explain what should count as a good result. Consequence: the response does not match expectations. Correct action: specify the tone, length, style, examples, and prohibited techniques.

The first answer is accepted without revision. Reason: the result seems convincing. Consequence: errors, repetitions, or inappropriate conclusions make it into the publication. Correct action: clarify the feedback, rewrite specific passages, and verify the facts.

How to improve a neural network's answer after the first result

A good prompt is rarely limited to a single message. A controlled dialogue provides more control: the user sees a draft, identifies specific shortcomings, and supplies the missing information.

1. Identify the problem. Specify exactly what was unsuitable: the tone, length, structure, incomplete answer, repetitions, or overly general conclusions.
2. Specify the exact action. Ask it to shorten the introduction, expand one section, replace bureaucratic wording, or restructure the table.
3. Add the missing data. Provide facts, examples, the brand's position, product specifications, or excerpts from the original document.
4. Ask it to show its assumptions. A separate list of assumptions helps identify places where the model went beyond the available data.
5. Check significant claims. Cross-check figures, names, dates, studies, and links against primary sources.

For SEO tasks, it is useful to separately check whether the model has substituted general recommendations for actual analysis. The tool generates hypotheses and drafts, but a decision requires data from the website, search results, and analytics.

When a ready-made prompt will not help

A request template cannot replace the original information. If the author has no data about the product, audience, or situation, the model will fill the gaps with assumptions. The same risk arises when a question requires up-to-date information but the user has not specified the date, source, and criteria for relevance.

Legal, medical, financial, and complex technical questions require verification by a qualified specialist. A neural network can explain terms, suggest a document structure or a list of questions, but it should not become the sole basis for a high-stakes decision.

Do not upload passwords, personal data, contracts, payment details, internal reports, or other confidential materials to public neural networks without an authorized processing procedure. We discuss the use of AI in Russian practice in the article on working with neural networks in Russia.

FAQ

What makes a prompt the best?

The best prompt is the one that produces the desired result for a specific task. There is no universal request for all neural networks, texts, images, and work scenarios.

Do prompts need to be written in English?

You can start in Russian. If the tool interprets the request ambiguously, try a more detailed formulation or a translation while preserving the context, constraints, and output format.

Which AI is better: ChatGPT, Claude, Gemini, or DeepSeek?

It is worth comparing not which one is “the best overall,” but its capabilities for a specific scenario: working with long materials, text style, file analysis, available features, image generation, and data requirements.

Can AI be used to write a thesis?

A neural network can help create an outline, explain the topic, suggest questions for sources, and check the logic of the argument. The originality of the work, reliability of the sources, and the educational institution's rules remain the author's responsibility.

Why does a neural network make up facts and links?

The model generates a probabilistic answer rather than conducting a guaranteed verification of every claim. Asking it not to make up facts reduces the risk, but figures, dates, studies, and links still need to be cross-checked.

How can you tell that a prompt needs revision?

Revision is needed if the answer is too general, violates the format, fails to take the audience into account, or contains assumptions instead of data. Identify the specific shortcoming, add context, and ask it to rewrite the relevant passage.

• First, formulate the task and the expected result.
• Provide the neural network with context and verified source data.
• Set the constraints, response format, and quality criteria.
• Revise the draft based on specific feedback and verify the facts.

The best prompt does not help you “guess” a neural network's answer; it frames the task so that the result can be checked and improved. SEO Mind42 publishes practical analyses of AI tools and SEO processes so that neural networks remain assistants in the work rather than sources of unverified solutions.

Paid access via API

If free limits are insufficient, access to models via API can be arranged directly with the vendor or through the Clodex partner service—below is a comparison of official prices and prices through the partner. For example, GPT-5.6 Terra through the partner is 28,6 times cheaper than the official price—the full list of models is in the table.