Prompt Engineering

Prompt Engineering is the practice of writing text queries (prompts) to large language models (Claude, GPT, Gemini) to obtain the desired result.

In 2024, the word became a hype word, in 2026 it is almost a curse word in the industry - because 80% of “guides on prompt engineering” are banal advice like “be specific.” Real practice is deeper.

A good prompt in my projects contains: - Role / context: “You are a performance marketer with 9 years of experience” - Task: “Write 5 headlines for VK Ads” - Constraints: “Every ≤30 characters, no emoji, in brand style” - Examples: 2–3 examples of good and bad headlines - Format: “Response is a JSON array of strings”

The main rule that I learned: a prompt is not a magic formula, it is a specification of a task. The more accurately you can describe your desired outcome in words, the better the LLM will do. If you yourself don’t know what you want, no prompt engineering will help.

Prompt engineering is becoming obsolete: the Claude 4.7 and GPT-5 models in 2026 no longer need** dozens of pages of prompts. They understand the intent in 2-3 sentences. Read more about my AI stack in tools review 2026.