The quality of a neural network's response is 80% dependent on how you formulate your query. The same AI can produce mediocre text or a brilliant result — the difference lies solely in the prompt. In this article, we break down 10 proven rules that will transform your queries from "average" to "excellent."
Why Prompts Are So Important
A neural network cannot read minds. It works strictly with the context you provide. A vague query = a vague answer. A specific query with the right structure = a precise and useful result.
These rules work for any language model — ChatGPT, Claude, Gemini, YandexGPT, GigaChat, and others.
Rule 1. Be Specific
The most common mistake is a query that is too general. The more specific you are in formulating the task, the more accurate the answer will be.
Bad:
Write a text about marketing.
Good:
Write a 1500-word article on content marketing for small businesses in the food service industry. The target audience is cafe and restaurant owners with a marketing budget of up to 50,000 rubles per month. Include 5 specific strategies with examples.
Specificity includes: topic, length, target audience, format, number of points, and constraints.
Rule 2. Provide Context
The neural network doesn't know who you are, who you're writing for, or what situation you're in. Give it this information.
Bad:
Help me create a plan.
Good:
I am a marketer at an IT company that sells a CRM system for small businesses. We need a content plan for March for our Telegram channel (5000 subscribers, B2B audience). We currently post 3 times a week. Help me create a monthly plan with topics, formats, and publication days.
Context allows the model to "tune in" to your situation and provide relevant advice, not general phrases.
Rule 3. Specify the Format and Structure
Indicate in what form you want to receive the answer. Neural networks work excellently with clear structural requirements.
Bad:
Tell me about the advantages of remote work.
Good:
Create a comparison table of remote and office work. Columns: Criterion, Remote (pros), Office (pros). Include at least 8 criteria: productivity, expenses, socialization, work-life balance, career growth, health, flexibility, teamwork.
Popular formats: list, table, step-by-step instructions, JSON, markdown, conversation script, SWOT analysis.
Rule 4. Use Examples (Few-Shot)
Show the neural network an example of the desired result. This is one of the most powerful techniques — the model "understands" the pattern and reproduces it.
Bad:
Write product descriptions for an online store.
Good:
Write product descriptions for an online electronics store. Here is an example format:
**Name:** SoundMax Pro Wireless Headphones
**For whom:** For those who value quality sound on the go
**Key advantages:**
- Active Noise Cancellation (ANC)
- 30 hours of battery life
- Weight only 250 g
**Price:** 4,990 ₽
Now write descriptions in the same format for:
1. BassBox Mini Portable Speaker
2. FitTrack S3 Smartwatch
3. StreamPro 4K Webcam
1–3 examples are usually enough for the model to grasp the desired style and structure.
Rule 5. Iterate and Refine
Don't expect a perfect result on the first try. Prompt engineering is a process. Get an answer → evaluate it → refine it.
First query:
Write a social media post about our new product.
Refinement after the answer:
Okay, but make the text shorter — up to 150 words. Add emojis. The tone should be friendly, not corporate. And add a call to action at the end.
Further refinement:
Great! Now make 3 versions of this post: one for Telegram, one for VKontakte, one for Instagram. Consider the specifics of each platform.
Each iteration brings you closer to the perfect result. Don't be afraid to give feedback to the model.
Rule 6. Use Chain of Thought
Ask the neural network to "think step by step." This significantly improves the quality of answers to complex questions, analytical tasks, and logic problems.
Bad:
What technology stack should I choose for a startup?
Good:
Help me choose a technology stack for an MVP of a services marketplace (similar to YouDo). Team: 2 full-stack developers. Budget is limited. Need to launch in 3 months.
Reason step by step:
1. First, define the key technical requirements
2. Then consider 3 stack options
3. For each option, list the pros, cons, and development timelines
4. Make a final recommendation with justification
Chain of Thought forces the model to break down the task into stages and arrive at a more thoughtful conclusion.
Rule 7. Use Negative Instructions
Specify what NOT to do. This helps avoid typical problems: fluff, clichés, irrelevant content.
Bad:
Write an article about healthy eating.
Good:
Write an article about healthy eating for busy professionals (25–40 years old).
DO NOT use:
- Phrases like "in the modern world," "it's no secret that," "as we know"
- General advice like "drink more water" without specific numbers
- References to the outdated food pyramid
- A mentoring or preachy tone
Write concretely, with numbers and practical advice that can be implemented in 5 minutes.
Negative instructions are especially useful for text content to avoid clichés and "fluff."
Rule 8. Assign a Role
Ask the neural network to "become" an expert in the required field. This changes the style, depth, and focus of the answer.
Bad:
How to prepare for a job interview?
Good:
You are an HR Director at a large IT company with 15 years of experience conducting interviews. You have conducted over 3000 interviews.
I am a middle Python developer with 3 years of experience, going for an interview at Yandex for a senior position. Help me prepare:
1. What questions will they definitely ask?
2. What do they pay the most attention to?
3. What mistakes do 90% of candidates make?
4. How can I stand out among other candidates?
Assigning a role doesn't change the model's "knowledge," but it affects the delivery, style, and emphasis in the answer.
Rule 9. Control Creativity
Different tasks require different levels of creativity. Indicate this in the prompt, even if you can't directly adjust the temperature.
For a factual task (low creativity):
Create an accurate checklist for registering an LLC in Russia in 2026. Only verified data, no assumptions. If unsure about something — state that explicitly.
For a creative task (high creativity):
Come up with 20 unusual names for a cyberpunk-style coffee shop. Be as creative as possible, mix technological and coffee terms, use wordplay. The more unexpected, the better.
Key markers: "strictly factual" and "be precise" lower creativity; "surprise me" and "be original" increase it.
Rule 10. Break Down Complex Tasks into Subtasks
Don't try to solve everything with one prompt. It's better to break complex projects into stages.
Bad:
Create a complete business plan for a food delivery startup for me.
Good — a staged approach:
Step 1: Let's start with a market analysis of food delivery in Moscow. Who are the main players, what is the market structure, what niches are unoccupied?
Step 2: Based on the analysis above, suggest 3 positioning options for a new delivery service. For each option, specify the target audience, unique selling proposition (USP), and preliminary market size estimate.
Step 3: For option #2, create a financial model for the first year. Include startup investments, monthly expenses, revenue forecast, and break-even point.
Each stage builds on the result of the previous one, and the final quality is much higher.
Bonus. Universal Prompt Template
Let's combine all the rules into one template that can be adapted for any task:
[Role]: You are [an expert in field X with Y years of experience].
[Context]: I am [who you are], I need to [goal]. Situation: [description].
[Task]: Do [specific action].
[Format]: Present the result as [table/list/text of N words].
[Constraints]:
- Do NOT [what not to do]
- Consider [important conditions]
[Example]: Here is an example of the desired result: [example]
Reason step by step, start with [first step].
Common Mistakes When Writing Prompts
| Mistake | Why It's Bad | How to Fix It |
|---|---|---|
| Query too short | The model makes assumptions for you | Add context and details |
| Multiple tasks in one prompt | The model loses focus | Break it into separate queries |
| No example of the result | The answer format is unpredictable | Add an example (few-shot) |
| Abstract requirements | "Good text" — what does that mean? | Specify concrete criteria |
| Ignoring iterations | The first answer is rarely perfect | Refine and improve |
Conclusion
Prompt engineering is not magic, but a skill. The ten rules in this article cover 90% of situations when working with any language model. The main thing is practice: the more you experiment with formulations, the faster you find working approaches.
Start with the simplest thing: the next time you write a query to a neural network, add context, specify the format, and provide an example. You'll be surprised how much better the answers become.