Two people ask an AI assistant seemingly the same question – and get results that are as different as heaven and earth. One remains dissatisfied: “this neural network of yours is useless, it produces some kind of water.” The second receives a structured, precise, ready-to-use answer. The difference is not in the model – the difference is in how the request was formulated.
Prompting is not magic or secret knowledge of the elite. This is a skill that can be mastered with a few days of practice. Let’s look at specific techniques that immediately improve the quality of answers.
Why vague queries produce vague answers
The language model cannot read minds. It works with the text it receives and completes the answer based on patterns from the training data. The request “write about marketing” is technically correct, but extremely vague – the model has to guess: for whom the text is needed, how much volume is needed, in what tone, for what purpose.
Compare:
Weak request: “Tell me about email marketing”
Strong request: “Write a 5-letter email plan for an online clothing store that wants to bring back customers who haven’t purchased for more than 3 months. The tone is friendly, without being too intrusive. Each letter has a subject and a brief description of the content.”
The second query gives the model context, constraints, and a clear task. The result will be specific and applicable in practice, and not general discussions about the benefits of email marketing.
Principle 1: Set Context and Role
The language model changes the style and depth of the answer depending on who is “asking” and who the answer is intended for. Specifying the role helps the model select the correct case.
Example: “Explain what inflation is” will give a general school answer.
“Explain the concept of inflation to a person who has never studied economics, but wants to understand why food prices in the store are rising” – this request specifies the audience and the goal, and the answer will be more specific, with examples from everyday life, without unnecessary terminology.
You can also ask the model to play a certain role: “Pretend that you are an experienced editor and evaluate my text in terms of structure and logic.”
Principle 2: indicate format and volume
Without explicit instructions, the model itself decides how to format the response – sometimes it is a list, sometimes it is solid text, sometimes it is too long or too short.
Useful clarifications:
- “Answer in 3-4 sentences”
- “Set it as a table with three columns”
- “Give your answer in the form of a numbered list of 5 points”
- “Write without using jargon”
- “Make the text no longer than 200 words”
The more precisely the format is specified, the fewer edits will be required after receiving the response.
Principle 3: Break complex tasks into steps
Large tasks – writing an article, analyzing a document, developing a strategy – are better solved in stages, rather than in one giant request.
Instead of: “Write me a business plan for a coffee shop”
Better:
- “Help me draw up the structure of a business plan for a small coffee shop in a residential area”
- After receiving the structure – “Now expand the section about the target audience in detail”
- Then – “Now the financial model section with approximate figures”
This approach gives a deeper and more elaborate result at each stage, and allows you to adjust the direction as you work, rather than rewriting everything from scratch.
Principle 4: Few-shot prompting
If you need text in a certain style or with a specific structure, show the model a sample. This technique is called few-shot prompting and works surprisingly effectively.
Example: “Write 3 headlines for an article about healthy sleep in the style of these examples: – Why you wake up tired even if you slept for 8 hours – 5 habits that are quietly ruining your sleep Make headlines in the same spirit: specific, with a number or a question”
The model “reads” the pattern from the examples and reproduces a similar structure, tone and level of specificity.
Principle 5: Ask the model to clarify if something is unclear
You can directly ask the assistant not to guess, but to ask: “If you don’t have enough information for a quality answer, ask me clarifying questions before answering.” This is especially useful for complex or multi-valued problems where different interpretations produce completely different results.
Principle 6: State what not to do
Sometimes negative restrictions work more effectively than positive instructions.
Examples:
- “Do not use cliches and clichés like “in the modern world””
- “Don’t add an introduction, start straight from the point”
- “Don’t use bulleted lists, write in continuous text”
- “Don’t mention specific brands”
This is especially useful if the model repeats the same unwanted pattern over and over again.
Principle 7: Iteration is ok
The first answer is rarely perfect, and this is not a reason to consider the tool useless. Professionals who actively use AI usually do 3-5 iterations before getting the final result.
Useful clarifying queries:
- “Make the tone more informal”
- “Cut it in half, leave only the most important”
- “Add a specific example to the third paragraph”
- “Rewrite the second paragraph, it sounds too dry”
The conversational format is the main advantage of modern AI assistants compared to search. Use this: clarify, adjust, refine.
Good request template
For complex tasks it is convenient to keep the structure in mind:
Context (who am I, what is the situation) + Task (what needs to be done) + Audience (for whom) + Format (how to format it) + Constraints (what to avoid)
Not all elements are always needed – for simple questions it is redundant. But for work tasks, this structure saves several rounds of refinement.
Output
The quality of the neural network’s answer depends 70 percent on the quality of the question. This is not a drawback of the technology – this is its feature, like any powerful tool: with a pickaxe you can either build or get hurt, it all depends on how you hold it.
Start using at least two or three techniques from this article in your next requests – specify the role, format and specific restrictions. The difference in the quality of answers will become noticeable after the first attempts.