Prompt engineering: basics, examples and tools
Introduction
Artificial intelligence has become part of everyday work: from marketing and fintech to education. But the result does not depend on a "magic" model, but on the quality of the text query - the prompt. This is where prompt engineering: a skill that helps you turn fuzzy ideas into relevant and reproducible model output. If you are just starting to understand how neural networks work and where they are applied, a detailed breakdown is available in this article.
What is Prompt Engineering: Explained in simple words
Prompt Engineering - is the systematic writing of structured queries (prompts) to a linguistic or multimodal AI model to get predictable, high-quality answers for a specific task. This is not a "chat with a bot", but a mini-project: understand the goal, define the role of the model, constraints, output format, examples, and quality criteria. Unlike everyday questions, the basics of prompt engineering are important here: context, instructions, examples (few-shot), iterations and validation of the result. Clarity of wording has a direct impact on the quality outcome.
The main types of promts and their tasks
Promt type | When to use | Template/example wording | Expected output format |
Informational | There needs to be a simple explanation for a specific audience | "Explain what SEO optimization is to a small business owner" | Explanatory paragraph + everyday examples |
Instructional | Need options for actions, headings, steps | "Write 5 headline options for an article about the benefits of morning exercise" | Numbered list of 5 lines |
Role-playing | It's the tone, expertise and style that matters | "Imagine you are an experienced SMM professional. Give 3 ideas for a viral video for a sportswear brand" | 3 mini-concepts with a hook phrase |
Creative | Looking for a slogan, a name, an idea | "Come up with a slogan for a coffee shop near the university." | 7-10 short versions |
Iterative (refining) | Need to improve on the previous answer | "Make the previous answer more formal and shorter." | Edited text |
Evaluation/Review | Need criteria and edits | "Check the text for clericalisms and repetitions. Return the edits and explain why." | Marked corrections + comments |
Key tools for working with promts
ReText.AI - universal assistant for text tasks
ReText.AI closes the full cycle of a prompt-engineer's work: from idea warm-up to clean text and quality control. Below - how specific modules speed up and increase the predictability of the result.
- AI dialog mode. Uses: expand a business problem into a comprehensible prompt, test multiple tones and constraints (audience, length, format), commit a successful version as a template.
- Text shortening function. Application: quickly shorten a long article to understand its essence and compose an accurate prompt for analysis (clarify terms, KPIs, facts), make a short brief for the model.
- Clever paraphrasing (5 options). Style management without losing meaning. Application: turn "raw" wording into a clear instruction, match the tone to the target audience, compare options and choose the most understandable one for the model.
- Text completion tool. Application: from a short thesis, assemble a complete prompt context with examples, quality criteria, and response formats.
- Synonymizer. Lexical fine-tuning. Application: remove repetitive words in the prompt, replace ambiguous terms with more precise ones, reduce the risk of model misinterpretation.
- "Writer.". Workspace with auto-save. Usage: collect the result from blocks according to a template (introduction → thesis → conclusion), run paraphrasing/summarization in one window, see the history of edits.
Other popular neural networks
- Claude. Strong in reasoning and long context. Benefit for the prompt engineer: it is convenient to set role instructions, to run multi-step chains ("explain → analyze → propose format"), to keep a uniform style on large arrays of text, to quickly validate the reasoning of an answer using criteria from the prompt.
- Gemini. Convenient for "search → analysis → action" and multimodal scenarios. Usefulness: from a single query get an abstract of sources, a table with conclusions and short example formats (JSON/CSV); fits well into workflows with documents and presentations, where a structured, "office" result is needed.
- The visuals (logos and basic corporate identity) can be quickly assembled using the neural networks to create a logo.
In short: ReText.AI is a workstation for precise wording and text refinement; Claude and Gemini are prompt application environments where reasoning, long context, and convenient output to the task are important.
Example of a prompt engineering task: from idea to result
Scenario: creating a short block for an SEO article.
Step 1: Problem Statement.
Objective: "To write an introduction for an article on the benefits of yoga". Audience: office workers 25-40 with back pain and stress. Format: 150-200 words, friendly and motivating tone.
Step 2: Primary Promt (unsuccessful).
"Write an introduction about yoga." |
Problem: Without targeting and criteria, you get a faded, generic text: no segment, no length, no pain/benefit triggers, no call to action.
Step 3: Improved Promt (Lucky).
"Write an engaging introduction (150-200 words) for the article "10 reasons to do yoga in 2025." CA - office workers 25-40 who are bothered by stress and back pain. Style - friendly, motivational, avoid esotericism, add 1-2 specific benefits (sleep/sleep), finalize with a soft call-to-action." |
What will be the result: live text that speaks the language of CA, works with their pain, and offers a clear next task ("try a 5-minute complex in the morning") rather than generic phrases.
Step 4: Finalization with ReText.AI.
- Running it through Grammarian (remove repetitions and stamps).
- Doing 2-3 versions through paraphrasingand select the tone.
- If necessary Summarization up to 120-130 words per snippet.
- We check uniqueness and adjust the catchphrases.
Prompt Engineering: salary and demand in Russia
The market is still forming: some companies are looking for "clean" companies industrial engineersSome of them are specialists combining analytics, integrations and customization of AI-agents - we tell you more about them in detail here. According to fresh sources, in RF there are forks from 50-150k ₽ for Junior, from 150-250k ₽ for Middle, and up to 320k ₽ and above for Senior/leaders (especially in banks/IT products and agencies). Examples of vacancies can be viewed at hh.ru.
Salary benchmark (₽/month, for 2025)
Level | Moscow/SPb | Regions | Comments/Sources |
Junior | 80 000-150 000 | 50 000-120 000 | Intern and jun positions from 50,000 ₽; basic tasks, A/B prompts, text review. |
Middle | 150 000-250 000 | 120 000-200 000 | Independent projects, setting up pipelines, working with data/integrations. |
Senior/Lead | 250 000-320 000+ | 200 000-280 000 | Banks/fintech/product: agent systems design, quality metrics. There are offfers up to 320,000 ₽. |
Where skill is especially needed: fintech and banks (regulations and accuracy), marketing and content production (speed/scale), EdTech (templates and personalization), telecom and e-commerce (conversational assistants). Market reviews note that prompt engineering often becomes an end-to-end skill for analysts, developers and content teams.
Most importantly
Before moving on to practice, let's capture the key findings.
- Prompt engineering is not about talking "in general", but about designing clear requests for a business goal.
- The backbone of the methodology: context → role → instruction → examples → format → criteria → iterations (the basics of prompt engineering).
- Tools: use ReText.AI for a quick cycle of "idea → draft → edits → check".
- Always break down an example prompt engineering problem into steps: statement → failed draft → improved prompt → finalization.
- Prompt engineering, salary and career: juns start from 50-150k ₽, middles 150-250k ₽, syns 250-320k ₽+ in IT/fintech/marketing (look specifically at responsibilities and stack).