النسخة العربية: كيفية كتابة برومبت احترافي للذكاء الاصطناعي
A useful AI prompt does not need complicated language. It needs a clear task and the information a model requires to produce the right kind of output. A request such as “write a good article” leaves the topic, audience, length, tone, and structure open to guesswork. Defining those elements makes the first response more useful.
This practical guide explains the components of a strong prompt, how to improve an existing request, and when to remove filler or add context, constraints, and examples.
What is an AI prompt?
A prompt is the text or set of instructions given to an AI model. It can be a short question, a detailed task, or a structured package containing source material, examples, rules, and a required output format.
Prompt quality is not the same as prompt length. A long request can remain vague and repetitive, while a concise, well-structured request can produce a precise result.
The five parts of a strong prompt
1. Role
Assign a relevant perspective, such as “act as a data analyst” or “you are an ecommerce copywriter.” A role should influence the expertise, language, or decision criteria used in the answer.
2. Task
Use a clear action verb: analyse, compare, summarise, recommend, rewrite, or create. A specific task reduces the chance that the model chooses an unintended direction.
3. Context
Provide the background that changes the answer, including the product, audience, objective, available data, or problem. Useful context is more valuable than a long description containing no new information.
4. Output format
State whether you need a table, checklist, email, executive summary, code block, or JSON. You can also specify headings, columns, language, and length.
5. Constraints
Define boundaries such as word count, tone, permitted sources, audience level, deadline, or facts that must not be assumed. Replace vague words such as “brief” with a measurable limit.
Use an AI Prompt Optimizer
Manarty’s free AI Prompt Optimizer helps review a request before it is sent to a model. You can paste an existing prompt to receive an analysis, quality score, and optimised version, or build a new prompt through structured fields.
The tool checks for elements such as role, task, context, format, and constraints. Its optional cleanup removes filler, repetition, extra whitespace, and vague wording, while displaying estimated tokens before and after optimisation. Processing takes place in the browser, so prompts are not sent to an external server.
How to optimise an existing prompt
- Paste the real request. Use the prompt you actually intend to submit.
- Review the analysis. Look at which components are present or missing instead of relying only on the score.
- Remove filler. Delete pleasantries and repetition that do not change the task.
- Replace vague wording. Turn “detailed” or “short” into a defined structure or length.
- Add context and constraints. Supply information the model cannot infer reliably.
- Test the improved prompt. Evaluate the output and revise the instruction based on a specific problem.
A before-and-after example
Before: Write a good advertisement for my shop.
After: You are an ecommerce copywriter. Create three English ad variations for a speciality coffee shop targeting first-time customers. Keep each variation between 40 and 60 words, use a friendly tone, communicate one clear benefit, and finish with one call to action. Do not invent prices or discounts.
The second version defines the role, product, audience, quantity, length, tone, and boundaries. The model has less to guess and the result is easier to evaluate.
How to save tokens without weakening a prompt
Reducing token use should not mean deleting valuable context. The goal is to remove text that carries no information while preserving everything that narrows the answer.
- Remove long pleasantries unless they define the desired tone.
- Combine repeated instructions into one clear constraint.
- Use short labels to separate context, task, and output.
- Avoid repeating the same data in multiple sections.
- Keep examples and rules that materially affect correctness.
Prompt examples for common tasks
Summarisation prompt
“Summarise the following text in five bullets for a non-technical manager. Preserve important figures and dates, and do not add information from outside the supplied text.”
Data-analysis prompt
“Analyse the attached table and identify the three largest month-over-month changes. Return a table with metric, previous value, new value, and percentage change. Then list any missing data that prevents a reliable conclusion.”
Email-writing prompt
“Write a professional, friendly email of no more than 120 words reminding a client about a Tuesday meeting. Include a clear subject line, and do not invent the meeting time if it is not provided.”
Common prompt-writing mistakes
- Combining several unrelated tasks in one request.
- Using subjective descriptions without a measurable standard.
- Requesting sources or figures without providing suitable references.
- Assigning a broad role that adds no value to the task.
- Creating conflicting constraints, such as demanding comprehensive coverage in very few words.
- Accepting the first output without testing and refining the prompt.
How to reduce inaccurate answers
Include reference material when it is available, ask the model to distinguish facts from assumptions, and tell it to identify missing information. For current or high-stakes topics, verify claims with appropriate external sources rather than relying on confident wording.
System prompts and regular prompts
A system prompt generally defines persistent behaviour, role, or rules for a conversation or application. A regular user prompt describes the immediate request. When an interface supports system instructions, place stable rules there and keep task-specific details in the current request.
Frequently asked questions
Is a longer prompt always better?
No. The best prompt contains the necessary information without needless repetition. Complex tasks may need substantial context, while simple tasks can use short instructions.
Does removing filler reduce answer quality?
Not when only words without useful meaning are removed. Deleting important context, constraints, or examples can weaken the result.
Do the same principles work with different AI models?
Clarity, context, and explicit output requirements remain useful across most models, but behaviour varies. Test the prompt with the model and workflow you actually use.
Does every prompt need a role?
No. Add a role when a particular perspective, expertise level, or style matters. A direct factual transformation may not require one.
Should I include an example?
An example is useful when format, tone, or classification rules are difficult to describe. Keep it representative and ensure it does not conflict with the written instructions.
Final takeaway
An effective prompt explains who should perform the task, what must be done, why the context matters, which output format to use, and what constraints apply. Start with a simple structure, test the output, and add details that correct a real problem instead of increasing length without purpose.