Prompt Engineering:
a complete guide to better results
How to write effective instructions to get accurate, relevant and useful responses from generative AI models. Techniques, examples and a reusable structure.
12 minute read
The discipline of writing
effective instructions for AI
Prompt Engineering is the discipline of formulating effective instructions to obtain more accurate, relevant and useful responses from generative artificial intelligence systems.
As AI-based tools become widespread — chatbots, virtual assistants, text, image and code generators — the ability to write effective prompts has become an increasingly sought-after skill in the workplace and in digital marketing.
In simple terms, a prompt is the instruction given to an artificial intelligence model. The quality of the result depends largely on the quality of the request.
Generic vs optimised:
the difference is immediately visible
Many people use AI with generic requests and get mediocre results. The same artificial intelligence can instead produce highly professional content when given detailed instructions.
Generic prompt
Write an article about marketing.Optimised prompt
Act as a digital marketing consultant
with 15 years of experience. Write a 1,500-word
article on digital marketing for small and
medium-sized businesses, covering SEO strategies,
online advertising, social media marketing
and lead generation. Use professional but
accessible language.In the second case the result will generally be more precise and complete. The model cannot read minds: it needs to know the role, objective, context, length and tone.
What you need
to make the model understand
A good prompt provides information that helps the model understand:
- The objective of the request
- Context
- Target audience
- Desired format
- Level of depth
- Communicative style
The more detail you provide, the greater the likelihood of getting a useful response.
The anatomy
of an effective prompt
1. Define a role
Assigning a role to the AI helps to steer the type of response. Examples:
- Act as a lawyer specialising in civil law.
- Act as an SEO consultant.
- Act as a science communicator with a medical background.
- Act as a university lecturer.
2. Specify the objective
State clearly what you want to achieve:
- Write a guide.
- Create a marketing plan.
- Summarise a document.
- Analyse this data.
- Generate content ideas.
3. Provide context
AI works better when it knows the context of the request. Example:
I am launching an organic products e-commerce
targeting customers aged 25 to 45.This information allows the output to be tailored to your needs.
4. Specify the format
Specifying the format reduces errors:
- Bullet-point list
- Table
- SEO article
- Technical guide
- Professional email
- LinkedIn post
- FAQ
5. Specify the style
The tone of the response can be adapted:
- Professional
- Formal
- Informal
- Technical
- Academic
- Commercial
- Persuasive
Beyond the basic prompt:
four useful patterns
Chain of Thought
This involves asking the model to reason step by step.
Analyse the problem step by step
and explain each stage of the reasoning.This technique is particularly useful for:
- Mathematics
- Data analysis
- Programming
- Problem solving
Few-Shot Prompting
You provide a few examples before making the request.
Input:
Product: Smartphone → Category: Technology
Product: Running shoes → Category: Sport
Now classify:
Product: Mountain bikeThe model will better understand the task required.
Prompt with constraints
Adding constraints improves control over the output.
Write an 800-word article.
Use short paragraphs.
Include at least 5 H2 subheadings.
Avoid complex technical terms.Iterative prompt
The best results are often achieved through multiple interactions. Typical process:
- First draft
- Review
- Improvement
- Final optimisation
The AI can be guided progressively towards the desired result.
What to avoid
from the very first prompt
Being too generic
Prompt: Tell me about SEO.
Better: Explain the most effective SEO techniques for an online fashion store.
Lack of context
Without information about the sector, target audience or objective, the result will often be too generic to be useful.
Overly complex requests in a single prompt
It is often preferable to break complex tasks down into multiple steps. For example:
- Analyse the market.
- Identify the competitors.
- Create a strategy.
- Write the operational plan.
Where prompt engineering
changes day-to-day work
Digital marketing
- Content marketing
- SEO
- Email marketing
- Social media
Business
- Document analysis
- Reporting
- Process automation
- Customer support
Software development
- Code generation
- Debugging
- Technical documentation
Training and education
- Course creation
- Teaching materials
- Quizzes and tests
Research and analysis
- Information synthesis
- Data analysis
- Report production
A professional structure
for a prompt
You can use this universal template as a starting point:
Role:
Act as [expert].
Objective:
[Describe what you want to achieve].
Context:
[Provide relevant information].
Format:
[Article, table, list, email, etc.]
Style:
[Professional, technical, informal, etc.]
Constraints:
[Length, structure, specific requirements].
Output:
[Precise description of the desired result].The future of Prompt Engineering
As generative artificial intelligence becomes ever more widespread, Prompt Engineering is becoming a strategic skill for professionals, businesses, marketers, developers and digital creators.
Knowing how to communicate effectively with AI models allows you to increase productivity, output quality and automation capability. For this reason, Prompt Engineering is today considered one of the most important digital skills in the age of artificial intelligence.
Frequently asked
questions
Will prompt engineering become obsolete as models improve?
Models already handle vaguer requests far better than two years ago, but the underlying principle hasn't changed: the more context you provide, the better the output. What disappears are the baroque syntactic tricks; what remains is the ability to express clearly what you want, in which format, and for which audience. It's a communication skill, not merely a technical one.
How important is it to write prompts in English?
On leading models (Claude, GPT-4, Gemini) quality in other major languages is excellent — there is practically no gap compared to English. For highly niche creative or terminology-specific tasks English may still hold a slight edge, but for 95% of business use writing in your own language works perfectly well.
Can I reuse the same prompt across different models?
Almost always yes, but with minor adjustments: each model has its own preferences for how prompts should be structured and how system instructions should be handled. A well-written prompt (role + objective + context + format) is about 90% portable; the remaining 10% of tuning has to be done through experimentation.
How long should a good prompt be?
As long as it needs to be, and no longer. For a simple task (rephrasing a sentence) 20 words are enough. For a complex task with precise constraints and format, 200–400 words is easily reached. Tokens sent = tokens paid: adding context is only worthwhile if it changes the output.
Is it useful to include examples in the prompt (few-shot)?
Yes, especially for classification tasks, structured extraction or output in a specific format. Two or three well-chosen examples often do more than a lengthy textual explanation. For creative or brainstorming tasks, however, examples risk limiting variety — it is better to describe the rules instead.
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