How to Write Prompts: The Complete 2026 Guide
In short
A prompt is the text instruction an AI model builds its answer from, so the quality of the result depends almost entirely on the quality of the request. Method beats template collections: a ready-made template solves one task, a method solves any of them. A strong prompt is assembled from five blocks — role, context, task, format and acceptance criteria. Proven techniques lift the result noticeably: persona assignment, few-shot (examples), chain-of-thought (step-by-step reasoning), data markup, decomposition and explicit acceptance criteria. The usual mistakes are a vague task, a missing format, and data mixed into the instruction. ChatGPT, Claude and Gemini respond to the same tricks differently, and prompts for coding agents (Claude Code, Cursor) are a separate discipline, closer to a written spec. Ready-made wordings live in our prompt collection and the arsenal; below is how to write your own from scratch.
What a prompt is and why method beats templates
A prompt is a natural-language request to a model that both sets the task and draws the boundaries of the answer. The model does not "understand" you in the human sense — it completes your text with the most probable continuation. So the more precisely you describe what you need, for whom and in what shape, the more predictable the result.
Ready-made templates save time on routine work and are handy to keep close by — for example, in the collection of ready prompts or the prompt arsenal with cards for common tasks. But a template breaks on a non-standard task: as soon as the context shifts, you have to rebuild the request anyway. A method gives you independence — you stop hunting for the "perfect prompt" and start constructing one.
What makes up a strong prompt?
Here is the anatomy, in five blocks. Not every one is needed every time, but the harder the task, the more it matters to assemble them together.
- Role tells the model whose voice to answer in. "You are an editor at a business magazine" narrows both the style and the quality bar: one sentence sets the vocabulary and the level of nitpicking about details.
- Context is the background of the task: input data, audience, constraints, goal. Without it the model guesses, and guesses wrong more often.
- Task is the specific action you expect: "shorten", "compare", "write a plan". One prompt — one main task.
- Format is the structure of the answer: list, table, JSON, length, tone. If you don't set a format, you get a random one.
- Criteria are the signs by which you accept or reject the answer: "no corporate jargon", "no longer than 200 words", "with code samples".
A useful habit is to word the task as carefully as you would a spec for a contractor. There's a separate breakdown of this: how to state a task so the agent gets it.
Which techniques improve the answer?
On top of the anatomy sit techniques long proven in practice.
| Technique | What it does | When to use it |
|---|---|---|
| Persona | Sets an expert frame and a style | You need a specialist-level answer |
| Few-shot | Shows 1-3 "input → output" examples | You need a strict format or style |
| Chain-of-thought | Asks the model to reason step by step | Logic, calculations, error analysis |
| Markup (XML/Markdown) | Separates instruction from data | Long context, many blocks |
| Decomposition | Splits the task into subtasks | Complex multi-stage result |
| Acceptance criteria | Describes what a good answer looks like | You need predictability |
Few-shot is the technique of showing the model one to three pairs of "question — model answer" so it copies the format. Chain-of-thought is asking it to reason step by step ("think step by step"), which cuts logical errors on tasks involving arithmetic and chains of inference. Markup is a simple but underrated tool: wrap the source data in tags or blocks (<text>...</text>) and the model stops confusing instruction with material. That matters most in long requests.
Which mistakes kill a prompt most often?
- A vague task. "Write about marketing" — the model picks the topic for you, and usually the wrong one.
- No format, no criteria. Without them the answer differs every time and you end up rewriting it.
- Data mixed into the instruction. When the material to process and the command are fused into one paragraph, the model gets lost — markup fixes this.
- Ten tasks in one prompt. Better to decompose: first the outline, then the text as a separate step.
- Polite vagueness. "It would be nice to have something like..." is read literally, as uncertainty. Be specific.
- Expecting mind-reading. If you need a particular style, show an example instead of piling on adjectives.
How do prompts differ for ChatGPT, Claude and Gemini?
The basic principles are universal, but the emphasis differs. As of July 2026 the current lines are GPT-5.5, Claude Opus 4.x and Gemini 3.x.
- ChatGPT (OpenAI). An all-rounder that holds explicit structure and system instructions well. Officially unavailable in Russia: you can't pay for a subscription with a Russian card, and the legal routes are a foreign card or a Russian reseller service working through the official API (as of July 2026). Details are in the guide on using ChatGPT in Russia.
- Claude (Anthropic). Strong on long texts and code, and unusually responsive to XML markup and explicit criteria. Also unavailable directly from Russia, and it bans accounts aggressively, so working through resellers on the official API is more stable. The finer points are in our breakdown of prompts for Claude.
- Gemini (Google). Large context and multimodality, tightly wired into the Google ecosystem. Plans are split into Free, AI Plus, AI Pro and AI Ultra; Russian cards are declined, so you need a foreign payment method (as of July 2026).
The practical takeaway: don't port a prompt between models word for word. Check the format, strengthen markup for Claude, and make step-by-step reasoning explicit for reasoning modes.
How do you write prompts for coding agents?
A coding agent is a model that sees your files and takes actions: writes code, runs commands, edits the project. That makes a prompt for an agent closer to a technical spec than to a chat question — precise framing, definition of done and boundaries all matter ("don't touch production", "cover it with tests"). It pays to say up front which files the agent may change and which it may only read: this sharply cuts the number of stray edits and rollbacks. Vibe-coding techniques are collected in a separate piece on practical techniques.
When there are many such tasks, scattered prompts turn into a system. The Quest vibe-coding engine packages exactly this methodology into a reproducible process for Claude Code and Cursor: roles, rules, criteria and ready scenarios so the agent behaves predictably. To learn it step by step there are the learning tracks, and for concrete wordings, the prompt arsenal.
FAQ
Where do I start if a prompt isn't working?
Break it down into the five blocks: is there a role, is there context, is there a single task, is the format and criteria set? Most often what's missing is the format and an example. Add one sample of the answer you want (few-shot) — that's the fastest fix.
Do I need to write prompts in English?
For the large models the difference between Russian and English is small — write in whichever language is comfortable. English is sometimes slightly more precise on narrow technical topics, but not at the cost of losing meaning in translation.
How is a method different from ready-made prompts?
A ready-made prompt solves one specific task; a method is the skill of assembling a solution for any of them. Keep the template collection at hand, but learn to build your own: templates run out, tasks don't.
Do these techniques work for image and video generation?
Partly. Role and criteria matter less there; what decides the result is scene detail, style and composition. The logic is the same — specifics instead of generalities.
How do I get good at prompting quickly?
Keep your own library of prompts that worked and note down why. The learning tracks speed this up, as does practice on real tasks from the arsenal.