Prompt Elements: How to Structure the Perfect Query for an AI and Get Accurate Results

A prompt is a command for artificial intelligence. Its structure directly determines the quality of the output. A vague phrase yields vague results. A clear structure delivers precise outcomes. Prompt elements are the building blocks that form a query. The right combination of these blocks transforms a neural network from a generic text generator into a fully-fledged assistant.

Users often complain: "The AI doesn't understand me half the time." The cause isn't the model, but the prompt. Missing key components forces the algorithm to guess what you want. The result? Empty text, unsuitable styles, and wasted time.

This article breaks down each prompt element—how it works, where it's used, and common mistakes made by marketers, SMM specialists, and entrepreneurs.

Canonical Elements

The four essential parts of any effective prompt.

Element 1: Instruction (The Task) — The Most Critical Part

The instruction is the action verb. It tells the model what to do. Without it, a prompt becomes a question without intent. The AI doesn't know what you want.

A proper instruction starts with a verb: "Create," "Write," "Analyze," "Rewrite," "Formulate." The verb should include a measurable outcome. "Write a short post (150 words)" is better than "Write a post." The metric provides boundaries.

Poor Example: "I'd like some text about our products."

Good Example: "Create descriptions for five products for e-commerce listings, 100 words each, in our brand's style."

Marketers often err by using subjective language: "make it beautiful," "think of something creative." These are wishes, not instructions. The model doesn't know what you consider beautiful. Instead, specify: "in a minimalist style, using a white background and accents in #FF5733."

Stick to one instruction per prompt. Multiple tasks in one query lead to contradictions. If you need both a post and an image, split them into two requests. Prompt chaining is a technique of sequential queries, where each handles a specific stage.

Element 2: Context — The Background That Stops the AI from Making Things Up

Context is the information that helps the model understand the situation. It answers: for whom, where, under what conditions, and for what purpose. Lack of context forces the AI to make assumptions, which are often incorrect.

Good context is the minimum necessary information. Don't dump your company's entire history. It's enough to state: "You are writing for Instagram followers aged 25-35, interested in specialty coffee." This immediately narrows the focus and sets the tone.

Context for text differs from context for images.

For Text: Target audience, brand style, previous publications, tone of voice.

For Images: Style, era, artist, mood, lighting.

Example: "Create a portrait of a woman in the Art Nouveau style, with soft evening light, background is a blooming garden."

Mistake: Overloading with context. AI models have a limited context window. Extra data drowns out what's important. Test: If you remove a paragraph of context, does the output change? If not, it's likely redundant.

Element 3: Input Data — The Raw Material for the AI to Process

Input data is the raw material for the AI. This could be text to rewrite, a table to analyze, code for review, or a list of keywords. Without input data, the request asks for generation from thin air.

For marketing, input data includes product specs, customer reviews, statistics, and briefs.

For SMM, it's the post topic, hashtags, and keywords.

For analytics, it's datasets, reports, and metrics.

Example: "Here is a list of product reviews (insert 5 reviews). Analyze which problems are mentioned most frequently. Output the top 3 pain points in a table format: Problem, Frequency Mentioned, Quote."

Input data should be structured. Instead of "here is some text," use "Text: [text]." Instead of "data in the attachment," use "Data: [table]." This reduces parsing errors.

Mistake: Incomplete input data. The user asks to write a post but doesn't provide the topic, style, or constraints. The AI starts guessing, resulting in unsuitable content.

Element 4: Output Indicator / Response Format — Controlling the Result

The response format dictates how the result should look. This could be a list, table, JSON, code, markdown, 150-word text, or five headline variations. Without a format, the model chooses a random one that may not fit your needs.

Example: "Output the result as a table with three columns: Keyword, Search Volume, Competition." This is an explicit output indicator. The model understands the structure and avoids adding extra text.

For texts, the format defines length, structure (headings, paragraphs), and tone.

For code, it's the language, framework, and style.

For images, it's resolution, aspect ratio, and file format.

Mistake: Ignoring format. A user requests "briefly," but is that 50 words or 500? Specify "briefly (up to 100 words)" to provide a metric.

Advanced Elements

For when you need more than the basic four.

Element 5: Role / Persona — Narrowing Style and Depth

The role is the mask the model wears. "You are an experienced copywriter," "You are a dermatologist," "You are an SMM specialist in the coffee niche." The role immediately sets the lexicon, level of detail, and style.

A role acts as a filter. Without one, the model writes for a "general audience." With a role, it uses professional jargon understandable to the target audience.

Example: "You are an e-commerce marketing specialist focused on home goods. Write a unique selling proposition (USP) for a new line of saucepans."

Mistake: A role that's too vague. "You are an expert" doesn't work. You need specifics: experience, specialization, communication style.

Good Example: "You are an enthusiastic English teacher for teenagers. You ask one question at a time and are highly motivational."

The role is especially crucial for long dialogues. The system prompt in an API is a role that persists for the entire conversation. A well-defined role saves time on clarifications.

Element 6: Constraints — Setting Boundaries and Prohibitions

Constraints are rules the model must follow: text length, prohibition on mentioning competitors, tone (strict, friendly), format, mandatory keywords.

Example: "Write 150 words. The keyword 'prompt engineering' must appear twice. Do not mention competitors. Tone: friendly but professional." This is a set of constraints.

Constraints prevent model "hallucinations" (fabrications). If you don't specify "do not invent facts," the model might generate fictional statistics. The constraint "rely only on the provided data" solves this.

For images, constraints are the negative prompt. "No deformations, no extra limbs, no text in the background." These are explicit prohibitions that exclude common artifacts.

Element 7: Examples (Few-Shot) — In-Sample Templates That Define Logic

Examples are "input → output" pairs embedded in the prompt. They show the model what the answer should look like. Few-shot prompting uses several examples and often works better than lengthy explanations.

Example for review classification:

"Example 1: 'Product arrived quickly, packaging intact' → Category: Logistics

Example 2: 'Poor quality, broke after one day' → Category: Quality

Now classify: 'The operator was rude but solved the problem' → Category:"

Examples save tokens. Instead of a long format description, showing one or two examples is enough. The model copies the structure, tone, and length.

Mistake: Bad examples. If examples are inaccurate or contradictory, the model will copy the errors. Examples should be perfect templates.

System vs. User Prompts: Where Each Is Used

System Prompt: This defines the role and rules for the entire dialogue. It's set once at the start of a session.

Example: "You are a marketing assistant. You write content for Instagram. You respond concisely and can use emojis appropriately."

User Prompt: This is the specific task within the dialogue.

Example: "Write a post about the new coffee blend."

The system prompt sets the framework; the user prompt provides the specifics. This distinction is vital for APIs and corporate chatbots. The system prompt remains consistent, while user prompts change, enabling the creation of assistants that don't forget the rules.

How to Assemble Elements Into One Prompt

A step-by-step formula for text tasks.

Step 1: Choose the Role and Audience

Define who is writing and for whom. "You are an experienced copywriter specializing in e-commerce. Your audience is women aged 30-45 interested in home goods." This sets the style and vocabulary.

Step 2: Clearly Formulate the Task (Verb + Result)

Write the instruction with a metric. "Write five headline options for a product card, each up to 60 characters, include the keyword 'coffee shop,' emphasize eco-friendliness." Verb "write" + metric "5 options up to 60 chars."

Step 3: Provide Minimally Necessary Context

Add background: "Product: reusable bamboo cups. Target audience cares about sustainability. Competitors focus on price; we focus on quality." Context shouldn't exceed 30% of the total prompt.

Step 4: Specify the Response Format and Structure

Write: "Output the result as a numbered list. Each item: a headline, followed by a short description in parentheses (up to 20 words)." This gives the model a structure to copy.

Step 5: Add Constraints and Examples

Constraints: "Do not use the word 'cheap.' Do not mention competitors. Tone: friendly but professional."

Examples: "1. Eco-Cup That Saves the Planet (A stylish cup made from sustainable bamboo...)". The model copies the structure from the examples.

Image Prompt Formula

How to assemble elements for Midjourney, DALL-E, Stable Diffusion, etc.

Formula: Subject + Action + Style + Background + Lighting + Technical Parameters

Subject: The main focus. Action: What's happening. Style: Artist, era, movement. Background: The environment. Lighting: Time of day, mood. Technical Parameters: Resolution, aspect ratio. Example: "Photograph of a woman working on a laptop in a cafe, in a 2020s documentary photography style, soft morning light through a large window, background of wooden tables and coffee beans, 4K, aspect ratio 16:9, realistic, high detail."

Negative Prompt: What to Exclude from the Result

The negative prompt sets constraints for images. "Without deformations, without extra hands, without text on background, no watermarks." This removes common generator artifacts. Weighted prompts allow you to emphasize or de-emphasize elements using syntax like woman::1.5, laptop::1.2, cafe::0.8. The numbers represent the weight the model should give each object.

Modern Techniques to Enhance Elements

How prompt elements work with advanced methods.

Chain-of-Thought (CoT): Adding a Reasoning Chain

CoT is the request to "solve the problem step-by-step." Prompt elements in CoT: instruction ("solve stepwise"), context (the problem), input data, format ("each step on a new numbered line"). This increases accuracy for complex tasks. Example: "Solve this math problem step-by-step. Show each step with an explanation. Problem: [condition]. Format: Step 1: ..., Step 2: ..., Answer: ..."

Few-Shot + Chain-of-Thought: Examples with Intermediate Steps

Combining few-shot and CoT provides a sample of reasoning. "Here is a problem and its solution with steps: [example]. Now solve this new problem using the same step-by-step approach." The model copies not just the answer, but the logic.

Self-Consistency: Multiple Runs for Reliability

Self-consistency involves running the same task multiple times with different CoT paths, then selecting the most frequent answer. Prompt elements: instruction ("provide three solutions, each step-by-step"), input data, format ("three variants, then the final answer").

Self-Critique: Making the Model Critique Its Own Answer

A two-step prompt. First: "Solve the problem." Second: "Now critique this solution and suggest improvements." Elements: instruction, input, format, then a new instruction ("critique") and format ("list of flaws and an improved version").

Ask-Before-Answer: Clarifying Questions First, Answer Later

This technique asks the model to "if data is insufficient, ask clarifying questions first." Elements: instruction ("first, ask what is unclear"), context (the task), format ("questions in a list, then the answer after receiving data"). This reduces hallucinations.

Common Mistakes in Elements

Anti-patterns that kill quality.

Vague Instruction Without Specifics

Poor: "Write something interesting about coffee." Good: "Write an Instagram post about a new coffee blend, 100 words, mention chocolate notes, friendly tone, use emojis."

Contradictory Requirements in One Prompt

Poor: "Be very brief, but describe all functions in maximum detail." This is a contradiction. Good: "Describe the three main functions in three paragraphs of 30 words each."

Excessively Subjective Wording

Poor: "Make it genius, creative, inspiring." These words have no metric. Good: "Use metaphors, real-life examples, active verbs, in the style of Brian Tracy."

Too Much Irrelevant Context

Poor: Including company history, mission, vision, founder's bio for a simple promotional post. Good: Provide context that affects the result: "Target audience: mothers with kids. Promotion: discount at kid-friendly cafes. Valid until the end of the week."

Ignoring Model Parameters

Poor: Not adjusting parameters like 'temperature'. Good: For creative text, set temperature to 0.7. For analytical tasks, use 0.2 for precision. Prompt elements work better with correctly tuned parameters.

Practical Use Cases and Ready Templates

Real-world scenarios: how prompt elements work in business.

Case 1: SEO Article for a Blog

Task: Write a blog post "How to Choose a Cafe." Instruction: "Write an SEO article, 1500 words. Keyword 'city center cafe' appears 5 times." Context: "Readers are people looking for a place to work, interested in Wi-Fi, prices, atmosphere." Format: "Introduction, three selection criteria, conclusion, call to action." Constraints: "Do not mention competitors. Tone: friendly but expert." Examples: Provide sample H2/H3 headings like "Criterion 1: Location." Result: Article ranks in top 3 search results, brings in 30% new clients.

Case 2: Product Description for an Online Marketplace

Task: Create a description for a saucepan on Amazon/Wildberries. Instruction: "Write a product description, 200 words. Include keywords: 'saucepan with lid,' 'stainless steel,' 'induction compatible.'" Context: "Target audience: homemakers who value quality. Competitors are cheaper but lower quality." Format: "Three paragraphs: benefits, specifications, care instructions." Constraints: "Avoid the word 'cheap.' Focus on quality. Tone: confident." Examples: "A stainless steel saucepan isn't just cookware; it's an investment in your family's health." Result: Product page conversion increased by 15%, reviews improved.

Case 3: Marketing Image for SMM

Task: Create an image for a "New Coffee" post. Subject + Action: "A cup of coffee on a white background, steam rising." Style: "Minimalism, flat design, bright colors." Background: "White, with coffee bean splashes." Lighting: "Soft, daylight." Technical Parameters: "1080x1080, 4K, no text, no watermark." Negative Prompt: "No people, no text, no extra objects." Result: Image received 500+ likes, 50+ comments, 20+ profile visits.

Case 4: Review Analysis and Pain Point Identification

Task: Analyze 50 cafe reviews. Instruction: "Analyze the reviews. Identify the top 3 problems and their frequency." Context: "Cafe is in a business center. Clients are office workers." Input Data: List of reviews. Format: "Table: Problem, Percentage Mentioned, Quote." Constraints: "Do not invent problems. Rely solely on the text." Examples: "Problem: Slow service → 40% → 'Waited 15 minutes for a cappuccino.'" Result: Identified a barista training issue. After retraining, positive reviews improved by 30%.

Case 5: Long-Lived Assistant (System + User Prompts)

Task: Create an assistant for employee training. System Prompt: "You are an experienced mentor at our company. You respond concisely, ask clarifying questions if data is missing. Tone is supportive." User Prompt 1: "Write an instruction guide for a new barista." User Prompt 2: "Clarify which coffee machine model is used." User Prompt 3: "Adapt the guide for this specific machine." Result: Assistant reduced training time from 5 days to 2. New hire errors decreased by 40%.

Conclusion

How do you know you've mastered prompt writing? When the model delivers the desired result on the first try or needs only one minor edit. When you clearly see which elements address which needs. When experiments take minutes, not hours.

Next Steps:

1. Create a library of prompts for recurring tasks in your niche.
2. Train your team to write structured queries using a checklist.
3. Implement prompt engineering as a process: Plan → Compose → Test → Iterate.
4. Stay updated on new techniques (Chain-of-Thought reasoning, reasoning models) and test them on your tasks.

While competitors spend hours on edits, you'll get results in minutes. Mastering prompt elements is a competitive advantage in the world of AI.