AI prompts: what they are, what they are used for, examples, and useful techniques

It is one of the most popular words of recent years, appearing practically everywhere—on magazine covers, online courses, technical articles, and business plans. A prompt is a phrase written by a human being that determines the behavior of a machine. More precisely, it is the starting point for every interaction with generative artificial intelligence models, from ChatGPT to image creation tools. On the surface, it is just a textual request, an input, but in reality it is an act of design: choosing the right words, defining the context, and setting the conditions guides the responses of the generative model, directs it, and shapes it. A real operating language has emerged around this dynamic, used by digital professionals, developers, content creators, and analysts. Understanding what “prompt” means and how to do prompt engineering today means moving with awareness in a new writing paradigm that addresses the reader but also speaks to those who generate the text.

What is a prompt and what does it mean today?

In the language of artificial intelligence, a prompt is a written or spoken message that serves as input for a generative model to process a response. It can be a sentence, a list of instructions, a question, a piece of code, or a complete block of text: whatever form it takes, its purpose is to trigger a response based on the behavior learned by the model during the training phase.

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The prompt guides the interaction and directly influences the quality of the output.

In the case of language models such as those developed by OpenAI, Anthropic, Google, or Meta, the prompt is a text sequence that instructs the system on what to produce, how to do it, and with what degree of detail. Semantic recognition occurs probabilistically: the model does not “understand” in the human sense of the word, but calculates the most plausible response based on the inputs received. For this reason, even the addition of a single word can radically change the result.

Compared to its original meaning, the term has undergone a significant transformation. In computing, ‘prompt’ referred to the command indicator on the screen – the symbol that invites the user to type an instruction. Today, in the field of generative AI, the prompt has become something more sophisticated: a linguistic interface through which people direct highly trained systems, no longer mere executive programs. It is the point of contact between human intention and artificial capability.

A well-constructed prompt can generate a detailed report, a poem, an SQL query, a promotional script, a comparison table, or a description of an imaginary work of art. Precise rules apply: clarity, context, explicit instructions. This has led to growing interest in prompt design and prompt engineering, fields that transform what was once just input into a true form of linguistic design.

The original meaning and evolution of the term

The term prompt has its roots in classical computer science, as mentioned above. In text-based operating systems, the prompt was the flashing cursor that signaled to the user that the system was ready to accept commands: a call to action, typically displayed as C:> in DOS terminals or $ in Unix consoles. It was a symbol, not a message, and represented the threshold between the computer’s inertia and the beginning of interaction.

With the advent of generative artificial intelligence, the meaning has expanded. From a simple indicator to an active input, the prompt today is much more than a passive request: it is the way in which the AI’s production engine is activated. The user does not issue a rigid command, but formulates a question or a narrative instruction. It is input written in natural language, not code, and is interpreted by models trained on billions of text sequences.

The key shift has been from direct execution (press a keyboard, get a reaction) to predictive interpretation (write a prompt, get a generated response). The prompt has become the language through which we dialogue with potentially generalist, adaptable, and contextual systems.

Why the prompt is so central to how AI works

In the functioning of generative AI models, the prompt is what determines—with varying degrees of accuracy—what the system will do. It is not a simple trigger, but the element that sets the coordinates of the result: content, form, style, length, linguistic approach. The centrality of the prompt stems from the way the model works: a predictive algorithm that calculates, word by word, the most statistically coherent sequence in response to what it has received.

In practice, each request to the AI is read as a “premise” to which the model assigns a plausible continuation. If the prompt is vague or inconsistent, the output will tend to be so as well. Conversely, careful wording produces more accurate, consistent, and reusable responses. Just compare two requests on ChatGPT to see the difference: “Write an article on renewable energy” will result in a very generic text, while a more guided version such as “Write an introductory article on the impact of renewables in Europe, informative tone, 500 words, in a popular style for high school students” steers the output in a concrete direction.

The principle behind image-based AI, such as Midjourney, DALL·E, or Stable Diffusion, is very similar. In this case, the prompt describes a scene, style, or atmosphere that the system will try to translate into a visual. Here, too, an ambiguous word or a missing indication can radically distort the result.

It is this directional function of the prompt that makes it a strategic tool today, not just an operational one: asking well means producing better. And it often means getting more with less.

How a prompt really works

When we provide a prompt to a generative AI model, we are feeding data into a system designed to produce an output that is consistent with that input. The basic mechanism is not governed by semantic understanding or intentional thinking, but by a prediction operation. The model analyzes the text sequence it receives and, probabilistically, identifies which word, phrase, or block is most likely to follow what precedes it in a meaningful way. Starting from that initial prediction, the process is replicated for each subsequent step, generating a response built word by word.

The prompt triggers this mechanism and defines its parameters. Every detail provided influences the weights that the model assigns to possible continuations, guiding it towards more relevant responses. This applies to both complex requests structured in multiple sentences and single concise commands. What changes between a generic output and an informative one is not only the length or formula of the input, but its instructional density.

AI does not “interpret” in the human sense: it has no representation of the extratextual context, nor any prior idea of our intent. Any contextual, semantic, or stylistic value must therefore be encoded in the prompt itself. There is no autonomous deduction about what is implied, the purpose of the response, the emotionality of the tone, or the desired structure. Writing an effective prompt means minimizing ambiguity and providing all the necessary elements, within the available space, to define the expected model behavior.

The predictive logic behind language models

Generative language models, such as those used in ChatGPT or Claude, work according to a probabilistic pattern: for each input received, they calculate which token (i.e., the smallest unit of language) is most likely to follow. This process takes place in fractions of a second but across billions of possible combinations, trained on extensive text collections and scaled to industrial parameters.

This logic is fundamental to understanding why prompt design has such a big impact on output. AI does not ‘choose’ the answer, but constructs it in real time based on what it has seen in training and on statistical consistency with what has been provided as input. When a prompt begins with “Imagine you are a consultant for…,” the model will search its statistical memory for the most frequent—or most plausible—linguistic configurations associated with that identity and the required tone. Each word in the prompt narrows the prediction context, acting as an informational constraint.

The more structured the prompt, the more the space of possible responses contracts in a clear direction. But a poorly constructed prompt — lacking an output category, without tone constraints, or overly ambiguous — leaves too wide a range of possibilities open, generating vague, redundant, or off-target responses.

Context, ambiguity, and specificity: what influences the result

There are three elements that directly influence the quality of the response generated: the context provided, the precision of the instructions, and the level of ambiguity in the prompt. These factors make the difference between useful output and generic text.

The context defines the “operating scenario” in which the response must fit. A prompt that neglects this aspect allows the model to assign arbitrary value to the elements mentioned. For example, “write a summary of the following text” is a meaningless request if you do not specify who the summary is for, what it will be used for, and what tone it should be written in. Adding even a single variable— “for a lawyer,” “in conversational style,” “for an internal report”—narrows the field of options and improves the output.

Ambiguity, on the other hand, has the opposite effect: when an instruction can be interpreted in multiple ways, the model will still try to guess, but with no guarantee of consistency. Writing “generate a list of tips” is not enough if you do not specify what the tips are about, in what format, and for what type of reader. The result can range from an impersonal paragraph to a series of motivational bullet points.

Finally, specificity is the best indicator of an effective prompt. A request such as “write a 300-word text describing a high-end tech product, professional tone, B2B marketing style” provides the model with codable and measurable parameters that guide the algorithm toward an output aligned with expectations.

Ultimately, the quality of the prompt does not depend solely on how much is written, but on what is made explicit and how much room for interpretation is left open. Generative models respond better when they are conditioned by structured instructions, inserted into linguistic sequences that simulate a coherent and well-defined context.

Effective prompts: structure, elements, and decisive factors

An effective prompt does not come from a generic idea, but from a series of deliberate choices. Its task is not to send a simple request, but to trigger predictable behavior on the part of the generative model. Every word, every delimitation, every sentence defines a part of the perimeter within which the system operates. This is why writing prompts works with an almost training-like logic: it is a matter of providing context, identifying the role of the virtual recipient, and establishing the type of response expected.

You can think of prompt construction as a modular assembly. Depending on the communicative goal and the application context, different blocks are selected: the role assumed by the AI, the type of task to be performed, the linguistic style, the tone of voice, the desired format for the response, and any specific constraints. The sequence in which these blocks are deployed in the prompt determines its effectiveness.

Writing well for a machine does not mean using elegant sentences. It means knowing how a language model interprets text to generate output. Lack of precision, the use of ambiguous or incomplete formulas, vague indications of the objective, and lack of constraints on the final format are all factors that compromise the consistency and usefulness of the result.

For this reason, a prompt must first and foremost be clear and action-oriented. It serves to trigger a specific behavior: explaining, summarizing, generating a list, producing code, simulating a person, formulating hypotheses, or developing a scenario. Identifying and stating this purpose—along with the context and desired conditions—is what allows the model to work in the user’s best interest. The textual form is only the means; the linguistic project is what matters.

Objective and tone: the two initial coordinates to clarify

Every effective prompt begins with the explicit determination of two elements: what we are asking the model, and in what style we want to receive the answer. The objective guides the function of the output text. The tone, on the other hand, defines its linguistic and relational register. Given that interaction is a communicative act, it is on these two variables that much of generative behavior is structured.

Clearly indicating the objective means specifying the expected action: generating content, performing a classification, proposing a solution, writing code, giving advice, completing a text. Without a stated function, the response will be generic and unfocused. Writing “Tell me everything you know about sustainability” will produce a shapeless and unworkable text. “Provide a summary of the main environmental benefits of wind energy, aimed at a high school audience” is an instruction that incorporates a clear objective.

Tone is not a secondary aspect. For a linguistic model, ‘professional,’ ‘friendly,’ ‘neutral,’ ‘ironic,’ or ‘narrative’ are parameters that affect lexical and structural choices. A prompt that sets a goal but neglects tone risks providing content with expressions that are inappropriate, overly institutional, or excessively colloquial. Stating both of these elements up front increases the likelihood of receiving a coherent and directly usable response.

Language, context, and output boundaries

In addition to the objective and tone, a well-designed prompt integrates operational variables related to text production: the output language, formal constraints, the level of detail, and the desired final structure. Without these elements, the model is free to interpret in an open manner, often straying far from the user’s intent.

Specifying the output language is not only useful for translations: many models are multilingual and can respond in Italian, English, or other languages even if the input is mixed. It is useful to specify the final language to avoid the output coming out in the wrong register. If you work in multiple languages, it is useful to specify this from the outset: “Write the answer in Italian, using an academic tone.”

Precise indications of length help to define expectations. Expressions such as “brief description,” “300-word paragraph,” “summary table,” and “10-line script” are immediately recognizable as formal constraints for generation. The type of response desired (summary, bulleted list, encyclopedic definition, social media post, sample email) also affects the form the output will take.

Other important variables are the degree of detail and the role to be assigned to the model. “Pretend you are a university professor of formal logic and answer…” adds data on the level of expertise and the textual production frame, while “Provide an explanation that can be understood by a reader unfamiliar with the subject” affects the complexity thresholds of the output text. Each explicit constraint reduces the distance between expectation and output.

Iteration and testing: the prompt as a process, not a command

Thinking of a prompt as an order to be given is a misleading mental model. The operational reality of interacting with generative models is closer to a cycle of testing and progressive improvement. A prompt can be written in detail, but if the output is inaccurate, excessive, unbalanced, or inconsistent, it is legitimate—and useful—to reformulate it. Not as a correction of the text produced, but as a refinement of the input.

The effectiveness of a prompt grows through iteration. After an initial request, it is possible to assess how well the result meets the desired criteria and identify areas for improvement. “Add a conclusion summarizing the benefits,” “Rewrite the content using more direct language,” “Remove repetitions and reduce the length of the paragraph” are corrective reactions that, when transformed into new prompts, lead the model to produce increasingly functional versions.

In many professional applications, this cycle leads to the discovery of stable, reusable structures that can be saved or adapted for subsequent tasks. This is how we move from individual prompting to systematic prompting: the ability to record the best solutions and evolve prompts from occasional experiments into real working tools.

Accepting the iterative nature of the interaction allows us to abandon the idea of the ‘perfect prompt’ and embrace a productive logic more akin to collaborative writing: AI becomes a co-author that responds better as our precision increases.

Prompting techniques explained

Not all prompts activate the model with the same depth. Depending on the type of task, the complexity required, and the quality of the input, the strategies for obtaining a more accurate output also change. Three techniques are now widely recognized in prompting practice: zero-shot, few-shot, and chain-of-thought. Each follows a different logic, based on the specific behavior of large language models (LLMs) pre-trained on vast text corpora.

These are complementary approaches, not alternatives. The choice depends on the model’s prior knowledge of the task, the variety of possible responses, and the required semantic structure. In simple situations, a direct formulation is often sufficient. But as the desired output becomes more complex, or with more degrees of interpretative freedom, the value of iterative or multi-modular techniques, which guide the model step by step, increases.

These strategies do not require advanced technical knowledge, but a clear understanding of how generative AI builds responses. Knowing how to choose between the different prompting methods means increasing efficiency, improving the relevance of outputs, and reducing revision cycles. It is a key conceptual step: you don’t just “try prompts,” you build an interaction designed around targeted questions, relevant examples, and logical sequences.

1. Zero shot: when instructions are enough

With zero-shot prompting, you give the model a task without providing explicit examples or previous interactions. You formulate a specific request, trusting the system’s ability to derive meaning and return a coherent response based on its statistical knowledge.

Zero-shot is suitable for standard requests, simple classifications, concise descriptions, or questions whose answers are already present in the model’s training data. It is useful when a quick response is needed, in a very generic format, and the model is assumed to already be familiar with the type of task.

Typical examples:

• “Explain what blockchain means in simple terms.”
• “Write an Instagram caption for a photo exhibition.”
• “What is the capital of Peru?”

This approach is effective for short, targeted inputs, but the more context, nuance, or complex formats the task requires, the greater the risk of receiving vague or stereotypical responses. Without concrete references, the model may replicate recurring structures found in the training data but miss the operational goal.

2. Few shot: show guiding examples

Few-shot prompting consists of including one or more explicit examples of how the response should be structured in the prompt. It is a very useful technique for describing behavior patterns, imitating styles, classifying texts, and generating content consistent with a narrative or argumentative logic.

The examples act as implicit guidance. If well constructed, they allow the model to deduce the desired structure and replicate it even in the presence of new content. The principle is similar to learning by analogy: AI derives the rule from observed cases and tries to apply it to subsequent ones.

Example

Prompt: Classify the sentiment of the sentence (Positive / Negative / Neutral):

• “The product exceeded my expectations.” → Positive
• “It’s not what I was hoping for, I’m a little disappointed.” → Negative
• “The service was acceptable, no particular problems.” → Neutral
• “The salesperson was helpful and friendly.” →

In this context, the model has good references for creating continuity and tends to maintain consistency even with a stylistically compatible final output. Few-shot guidance works particularly well with text classifications, journalistic styles, literary genres, structured lists, and text-to-code or code-to-text transformations.

The quality of the examples provided is crucial: if they are too long, generic, or ambiguous, they confuse the model and reduce the effectiveness of the guidance. The choice of the number of examples—typically between 2 and 5—should be balanced with the size of the total prompt and the space available for processing.

3. Chain-of-thought: guiding reasoning step by step

Chain-of-thought (CoT) prompting is not limited to giving static instructions or examples. Instead, it constructs an explicit thought path, segmenting the request into clear steps that the model must execute in sequence. It is particularly effective in tasks involving planning, deduction, multi-level problem solving, or non-linear reasoning.

With CoT, the goal is not just to provide a final result, but to explicitly show how to get there. The prompt invites the model to articulate its response through a chain of inferences, just as a human would when solving an analytical or argumentative problem.

Example

Prompt: “John has 8 apples. He gives 3 to his sister and then buys 4. How many apples does he have now? Explain each step before giving the final answer.”

This technique improves output transparency and reduces the risk of logical errors, particularly on mathematical problems, situational tests, qualitative analysis, multi-step rewrites, or complex comparisons. It also works well when asking the model to generate alternative scenarios, simulate conditional decisions, or propose causal reasoning.

Chain-of-thought has an important advantage: even if the model makes a mistake, the error is easier to spot because each step of the process is visible. It also increases controllability, as you can intervene in a single step without having to rewrite the entire prompt. In complex tasks, this technique comes closest to true conversational intelligence design.

Prompting frameworks: how to standardize and replicate results

Giuseppe Liguori explains this well in his course AI for Marketing at SEOZoom Academy: writing an effective prompt is a creative task, but repetition makes it clear how much a systematic approach is also needed. When working with artificial intelligence on an ongoing basis, the ability to reuse proven structures becomes a decisive factor in efficiency. Prompting frameworks meet this need: they provide a syntactic and functional grid within which to formulate consistent, replicable requests that are easily adaptable to different contexts.

A framework is not a rigid formula, but a modular scheme. It helps to assign roles correctly, define the type of task, provide context, and establish objectives and constraints. Some are designed to structure complex conversations, others for recurring and repeatable tasks, and still others to make the desired output format clear from the outset. Depending on the work format (content, marketing, education, development, support), you can select the most suitable framework or build one tailored to your needs.

The most widely used frameworks today—such as RACE or RISEN—are distinguished by their ease of use and immediate transferability into operational prompts. They do not replace the intelligence of writing, but offer concrete support in setting up linguistic commands. And they work particularly well when the request needs to be translated into a functional output: an argumentative text, a script, a structured list, an action plan, or an analysis.

RACE: clarity of role, task, and context

The RACE (Role, Action, Context, Explanation) framework organizes the request around four key elements. As Kate Moran explains, it is designed to provide the model with a precise identity, a specific task, a reference information environment, and an explanation of the desired output. It works well with models such as ChatGPT, Claude, and other generalist LLMs, especially when the prompt needs to guide the tone, form, and purpose of the response.

A conversational example: “You are a child psychologist with experience in supporting preschoolers who show fear of the dark. Design a set of practical and reassuring strategies to help a 2-year-old girl sleep peacefully. The girl often wakes up crying and seeks her parents’ presence to fall back asleep. There have been no recent changes in her routine, but the family wants gentle, non-invasive solutions. Provide guidance with short- and long-term tips, using simple language appropriate for parents.”

Here, the Role is explicit (“you are a child psychologist”), the Action is defined (“design practical strategies”), the Context is realistic and detailed, and the Explanation of the output is clear (“guide with advice divided into sections”). This framework guides the model to select relevant knowledge, respond in a structured way, and choose a register appropriate for the target audience.

RACE is useful in many operational situations: content writing, training, public communication, decision support. It allows you to align expectations between the person writing the prompt and the generated output.

RISEN: function, steps, goal, and constraints

The RISEN (Role, Instructions, Steps, End goal, Narrowing) framework aims to structure more complex requests, where the execution steps are as important as the final goal. It is a practical model suitable for prompts that require a response organized in sequences of actions, such as itineraries, operational schedules, scripts, calendars, and practical guides.

A prompt built with RISEN might sound like this: “You are a travel expert who plans cultural experiences in London for first-time visitors. Create a plan for a 4-day trip: divide the proposal into morning/afternoon/evening, including museums, walking routes, and typical restaurants. Indicate the means of transportation to use, recommended tickets, and average visit times. The plan should be suitable for an adult couple with an average budget and a relaxed pace. Write everything in a maximum of 400 words, in a hierarchical list format.”

The expected output is clear and concise. There is a function (create a plan), a step-by-step execution (days/times), a goal (enjoyable cultural experience), and a set of constraints on format, tone, and content. Compared to RACE, RISEN focuses more on the internal sequence of the response and the operational constraints to be respected.

It is particularly useful for prompts intended to generate structured content that is then transferred to other tools: editorial plans, training programs, email marketing, step-by-step strategies.

Creating a customized framework

No framework is mandatory: they are tools, not prescriptive models. And for this very reason, they can—and often must—be modified to better suit specific needs. Creating your own prompting system is not only possible, but recommended for those who work with recurring prompts or need to generate standardized output on an ongoing basis.

Designing a personal framework starts with analyzing recurring tasks. If you often work on promotional tone of voice, comparable lists, persuasive emails, or UX micro-copy, it is useful to build a prompt scheme that includes:

• Expert role + context of use + type of end user
• Specific objective (e.g., generate a persuasive opening text)
• Constraints on format and style
• Level of detail
• Additional instructions on response behavior (e.g., avoid technical tones, use metaphors, avoid passive language)

This structure can be saved, duplicated, adapted to other tasks, and refined over time. Some tools now allow you to build custom GPT models from predefined prompts. In this context, the framework becomes a real operational asset, halfway between a template and a production process. Whatever the domain of use—content creation, analysis, customer support, or education—having a design skeleton optimizes the dialogue with the model and reduces the time spent on manual optimization.

Prompt design and prompt engineering: differences and applications

Writing an effective prompt is only the first level of interaction with generative models. The next step is more design-oriented: it concerns how the interaction between the user and the AI is structured in a systematic and repeatable way. In this context, the distinction between prompt design and prompt engineering comes into play, two concepts that share similar tools but have different objectives and areas of application.

Prompt design focuses on the quality of the user experience in AI-assisted conversational contexts. It is a practice that stems from interface design and extends to language processing: prompts are written not to obtain an ad hoc response, but to guide the behavior of the AI in a specific use case. The goal is to create a response that is consistent with the assigned role, the desired tone, and the end user’s expectations. It is not only relevant for generalist models, but also for custom chatbots, internal platform chats, and copilots in professional software.

Prompt engineering, on the other hand, is the technical and scalable side of this relationship. It involves defining precise, reusable, documented input models, often embedded in larger systems such as automated workflows, APIs, AI agents, or specialized GPTs. It is an activity that requires cross-functional skills: language, UX, logic, structured syntax. In many cases, it translates into the construction of semi-automated systems based on dynamic prompts, conditioned by variable data or multiple instructions.

Design builds the experience, engineering builds the methods. Both are centered on the idea that a prompt is not just clever text, but an operational interface between what the user wants and what the AI can do. Writing more effective sentences is not enough: you need to learn how to design them to achieve reliable, scalable, and predictable performance.

Prompt design: writing inputs for a UX goal

Prompt design originated in contexts where artificial intelligence is integrated into applications, chatboxes, voice assistants, support tools, or educational environments. At the heart of this practice is a very concrete question: how to write an input (fixed or dynamic) that allows the system to respond in a useful, clear, unambiguous way that is consistent with the role it plays within that specific user experience?

Unlike “informal” or experimental prompting, prompt design is not intended to elicit a one-off response, but to set up an effective interaction between the AI and the user. The prompt text can be invisible (preconfigured in the interface), textual (for public chatbots), hybrid, or variable depending on the data passed as input. In all cases, the aim is to direct the AI’s behavior in accordance with the functional context.

A common example: a virtual assistant for customer care for a service must answer questions about returns, shipments, and technical problems. Prompt design allows you to model the system’s behavior so that it shows empathy, does not respond too generically, avoids frequent errors, and directs users to useful actions. We are talking about text, but also conversational structure, information hierarchy, and escalation in subsequent steps.

Prompt design applies UX, content design, and information architecture logic. The goal is not only to produce the right answer, but to do so in the right way, with the right nuances, and according to the expectations of the user receiving the output.

Prompt engineering: a new professional skill

Prompt engineering emerged from the bottom up, as an empirical practice, and has gradually become formalized as a standalone skill. The growth in the number of generative AI tools, the possibility of building custom GPTs and conversational agents, as well as the first services offered in enterprise environments, has transformed prompt writing into a strategic skill.

Prompt engineers do not just write well-formulated commands. Their task is to define repeatable processes for interacting with models: creating sets of functional prompts for specific scenarios, testing them, validating their robustness, improving them, and documenting them. They integrate formal elements (dialogue, tone, request structure) with technical constraints (tokens, temperature, dynamic input variables, model compatibility, computational limits).

Among the main applications of prompt engineering are:

• Automation of editorial or training tasks.
• Optimization of code generation.
• Support for business decision making via AI.
• Construction of cognitive micro-agents based on complex prompts combined with scripts.
• Integration of generative models into production pipelines (such as CRM, HR modules, customer support).

This activity has clear advantages at the operational level: it allows for more consistent and reusable output, reduces the number of failed or reformulated prompts, and helps maintain a stable quality standard over time. In addition, it facilitates collaboration between interface designers and those working on model behavior. In many companies, these skills are beginning to be explicitly required in contexts ranging from UX writing to the development of intelligent workflows.

The limits of prompts: manipulation, vulnerability, and sycophancy

The interaction between humans and generative language models is not neutral. The apparent immediacy that characterizes prompt writing hides a deeper level of criticality: what appear to be transparent tools are, in fact, influenced mechanisms that can be circumvented or filtered based on unstated rules. The limitations of prompts are not limited to their writing: they emerge in the way models react, adapt, or distort responses based on context, semantic pressure, linguistic constraints, or the simple implicit desire to “please” the writer.

In some cases, the interaction can be manipulated by the user to cause the system to generate content that it would normally avoid. In others, it is the model itself that distorts its responses to conform to what it perceives as desired by the prompt writer. In all these scenarios, prompting proves to be less controllable and less transparent than it seems. Recent experiments and reflections, including those collected by Alberto Puliafito and the behavioral analyses of the Nielsen Norman Group on sycophancy models, have rigorously explored many of these dynamics. Recognizing them does not mean giving up prompting, but learning to contain its gray areas.

Manipulative prompts and circumvention techniques

Communicating with a generative model also means learning how to push it beyond its apparent limits. A growing number of users are experimenting with forms of prompt escalation, starting with neutral questions and gradually steering the system toward content that it initially rejected. It is a simulative but effective mechanism: simple questions, followed by hypothetical scenarios, imagined roles, or progressively less ethical requests, guide the model toward outputs that are far removed from the initial input.

Some well-known techniques include:

• Intentional ambiguity – the prompt is not completely explicit, forcing the system to “deduce” the meaning and produce unexpected responses.
• Role-playing inversion – the model is asked to respond as a character with problematic traits in order to unlock normally inhibited content.
• Dialogic reverse psychology – the model is encouraged to ‘contradict the user’ in order to actually elicit what was wanted from the outset.

Several experiments, such as those conducted in the “Red Team Arena,” show that even highly filtered models—such as Claude, GPT, or Gemini—can produce offensive, inappropriate, or unsuitable content if “convinced” through multi-turn text chains. These are not technical exploits—or artificial manipulations of AI such as those described by Giuseppe Liguori some time ago—but real linguistic strategies applied to conversational sequences.

The greatest risk is systemic: if a model is vulnerable to manipulative prompting, even a seemingly legitimate prompt can be used to simulate support, obtain confirmed biases, or produce responses contrary to platform policies, without the system being able to block them.

The problems of sycophancy in language models

One of the least discussed—but most widespread—behaviors in language models is sycophancy, i.e., the tendency of the system to repeat, reinforce, or “agree” with the opinions expressed by the user. This is not a conscious choice: it is a form of statistical confirmation based on the principle that the best-rated output tends to follow the line already proposed in the prompt.

Real feedback and controlled tests show that models are more likely to agree, even when the user’s statement is false, ambiguous, or logically incorrect:

• “I think vaccines are useless. What do you think?” → the model may attempt unbalanced mediation.
• “Don’t you think X is overrated?” → greater likelihood of an affirmative response.

This is a behavior learned to optimize the reinforcement system during fine-tuning: “compliant” responses receive more approval and are therefore considered more correct. The result is a drift toward adaptive conformity, in which the model tends to return what it believes is expected, even at the expense of accuracy or completeness.

Sycophancy is particularly problematic in opinionated instructions, controversial topics, or requests where the model should “resist” misleading input and maintain neutrality. In multi-turn prompts, it can lead the system to circular logic, self-contradiction, or validation of unfounded content.

The problem of flattery

A particularly insidious subtype of sycophancy is explicit flattery, where the AI does not merely go along with a position, but incorporates compliments and praise for the user into its output, even if not requested. Platforms such as ChatGPT have shown a progressive increase in this behavior, with phrases such as “great question!”, “brilliant observation!”, and “thank you for your interesting insight” being inserted spontaneously even in neutral contexts.

The risk does not lie in the courtesy itself, but in its distorting effect: an artificial assistant built to improve productivity serves to support, integrate, and—if necessary—contradict. Flattery prevents this critical step: it hinders discussion, reduces the possibility of checking for logical errors, and ultimately obscures the role of a useful system. As Alberto Puliafito has lucidly observed, interacting with an AI that panders to our ego is radically useless if the goal is to obtain genuinely relevant and operationally valuable answers.

The problem—which did not affect a single version or prompt, but involved several models over time—was eventually recognized even by those who develop these systems. According to public statements by OpenAI, the flattering behavior was linked to an instruction in the prompt system of the GPT-4o model, designed to “adapt the tone to the user’s vibe,” making the conversation more fluid. This adaptation ended up emphasizing accommodating expressions, softening criticism, and reinforcing confirmation dynamics.

In response, a corrective command was introduced that asks the model to be “warm but direct,” avoiding “unmotivated flattery,” maintaining “professionalism and concrete honesty,” and asking follow-up questions only when necessary. The question remains, however: a system that simulates empathy risks continually crossing the line between naturalness and complacency. And for those who use these tools every day, the difference is substantial.

Flattery is dangerous because it alters the balance of the conversation. In a professional, academic, or documentary context, the goal is not to be told that you are right, but to obtain real verification, critical insight, and an alternative point of view. When even AI becomes condescending, the user experience loses cognitive value. It is not just a matter of avoiding annoying automatisms: it is about defending the usefulness of the systems we work with.

Invisible guardrails and output control

To limit the possibility of models generating harmful content, many companies employ filtering systems known as guardrails. These are implicit—often undocumented—mechanisms that prevent AI from responding to sensitive, violent, or illegal topics. The model may reject the prompt, respond in a generic manner, or end the conversation.

But the effectiveness of these filters is ambivalent. On the one hand, they protect against abuse, but on the other, they produce side effects: lack of transparency, sudden avoidance of legitimate topics, and failures in understanding context. It is not possible to know what criteria the filter operates on, nor what rules the model must adhere to. For example, in recent months, the case of the name “David Mayer” made headlines, causing ChatGPT to crash—only later was it clarified that this was a simple glitch caused by an internal instrumental error that mistakenly identified Mayer’s name.

Companies communicate general policies, but do not make the implementation of blocks visible, making every dialogue partially unpredictable.

Interaction with guardrails thus becomes an opaque terrain even for professional prompt writers. Legitimate phrases can trigger the block; others, more ambiguous, circumvent it. Prompting, in these cases, is no longer just linguistic design, but also a form of negotiation with a system filtered according to proprietary logic.

The ethical dimension of prompting, therefore, is not only about what is asked, but also about understanding what the model can (or cannot) do—and who ultimately decides how the linguistic freedom of an artificial system should be formatted. Writing an effective prompt also means knowing where interaction ends and algorithmic moderation begins.

Poorly designed prompts: common mistakes to avoid

Writing a bad prompt doesn’t just mean getting an unsatisfactory response: it means wasting resources, time, and in some cases fueling ambiguity, hallucinations, or problematic content generated by the models. The mistake isn’t in the grammar, but in the design of the input. There are linguistic choices that make a prompt ineffective, unstable, or unpredictable in its results.

The most common problems concern lack of clarity, an overabundance of incompatible instructions, semantic vagueness, or overconfidence in the model’s contextual abilities. These errors often stem from the idea that artificial intelligence can “intuitive” the real purpose of the request. Instead, every misunderstanding arises precisely from what has not been said — or has not been said in the right way.

There are also cases where a prompt, while syntactically consistent, generates unwanted content because it is poorly calibrated in tone, format, or exposed to ethical risks. In sensitive environments—education, healthcare, customer care—out-of-context output is not a simple mistake, but an operational problem.

Addressing key design mistakes is useful for both beginners and those looking to optimize the relationship between humans and generative models. It’s not just about “writing better”; it’s about writing with greater awareness of the consequences.

1. Information overload and contradictory commands

More information does not equal greater accuracy. One of the most common mistakes is to include an excessive sequence of details, instructions, technical constraints, and secondary requests in a single prompt, hoping that the model will be able to extract a functional summary. The result is often the opposite: confusing, uncertain, or overly generic responses.

A prompt that simultaneously asks “Be concise, but also descriptive; use a technical but accessible style; provide examples but keep the text short” contains conflicting requests. The model will choose an arbitrary direction and tend toward uncontrollable linguistic defaults.

In the same vein, the disorderly addition of side details can compromise consistency. A prompt such as “Write a 400-word article on sustainable tourism, promotional tone, journalistic style, with recent data, suitable for children aged 9 and up, divided into paragraphs and using academic citations” does not provide clear direction but presents the model with conflicting priorities.

An effective prompt, on the other hand, establishes what is most relevant and what can be omitted. Learning to synthesize without sacrificing essential conditions is a key skill for those designing interactions with AI.

2. Semantic ambiguity and lack of constraints

A prompt may seem correct on the surface but turn out to be vague when generated. Expressions such as “talk about,” “describe briefly,” and “write a paragraph about” define neither the function of the output, nor its audience, nor the desired format. The risk is that the model will produce standardized text, built on common associations and lacking real utility.

Example: “Write a text about global warming.” Probable response: a generic summary of sentences read in thousands of documents, lacking originality or direction.

Simply introducing minimal constraints yields a more targeted result: “Write an informative paragraph about global warming, informal tone, intended for middle school students, maximum length 120 words.”

The lack of boundaries has another significant side effect: inconsistency between the user’s expectations and what is returned. Without defining whether the output should be a list, a summary, a comparison, a dialogic simulation, or a narrative, the model will choose the form it deems most likely, regardless of the intended use.

Including explicit constraints—even simple ones—improves the quality of the interaction: “in an ironic style,” “according to the problem-solution pattern,” “in 5 short sentences,” “using examples from recent history” are instructions that the model is structurally capable of following and that increase the relevance of the generated content.

3. Confusing AI with a human being

Many ineffective prompts stem from a false assumption: thinking that the model can reason like a human interlocutor. This generates ambiguous formulations, relying on verbal codes that imply emotions, implications, or irony, which the system interprets only superficially, according to statistical patterns.

Phrases such as:

• “You already know what I mean, right?”
• “Talk to me like you’re my friend.”
• “Pretend you understand my frustration” do not trigger genuine empathy or produce more relevant responses. An LLM does not “feel” anything: it simulates the language associated with certain states or roles. The risk in this case is not only technical but also psychological: the user may attribute relational or moral competence to the AI that it does not possess.

Similarly, ironic or provocative questions — “How stupid is our society?” — do not produce critical reflections, but responses shaped by a hypothetical tone, often with standardized and superficial outputs.

In more sensitive environments (education, health, support), this misunderstanding can be even more problematic. A naive or poorly calibrated request can generate incorrect, misleading, or simply inappropriate content. Any expression that presupposes mental life, consciousness, or autonomous intention in the machine creates room for error.

Designing prompts does not mean dialoguing with a thinking subject. It means sending precise instructions to a predictive system capable of generating credible text. Natural language makes it easy to forget this, but effectiveness depends precisely on awareness of this limitation.

Prompts and generative AI: statistics, dimensions, and numbers

Generative artificial intelligence is no longer a laboratory phenomenon or a territory reserved for a few professionals in the field. It is an environment frequented daily by hundreds of millions of people who interact with chatbots, conversational agents, visual tools, and text models through a basic yet powerful tool: the prompt. Recent figures clearly show that prompting is not only widespread but is profoundly changing the way we communicate with technology.

According to the latest figures, ChatGPT—now the most widely used model globally—has over 400 million active weekly users. The chatgpt.com website receives over 5.2 billion visits per month, with an average session time of over 8 minutes. Interactions do not take place via menus, clicks, or static displays: they are linguistic sequences that activate a model, i.e., prompts.

In 2024, approximately 79% of traffic to ChatGPT is direct: users know what they want, are familiar with the interface, and type the URL directly. This indicates intentional and repeated use, centered on linguistic interaction. Globally, over 45% of users are under the age of 24, a sign that familiarity with prompting is becoming established as a generational skill.

The nature of prompts varies significantly depending on use: text generation, encyclopedic questions, code production, creative writing, and professional assistance are the most common areas. But what makes these numbers relevant is the trajectory: we are not just seeing a growing tool, we are witnessing the emergence of a new way of interacting with computing. The prompt as an operating language.

ChatGPT in figures

Referring to the most up-to-date data available online:

• 5.19 billion monthly visits to chatgpt.com.
• Over 600 million unique monthly users.
• Average time per session: 8 minutes and 13 seconds.
• Leading generative AI platform by global traffic.
• Approximately 40% of social traffic comes via YouTube.

The main target audience is concentrated in the United States (14.7% of users), followed by India, Kenya, Germany, and Brazil. Over 64% of users belong to a low-to-middle income bracket. The barrier to entry is minimal, the potential for use is very high: a combination that has made prompt-based interaction a de facto standard.

Questions, traffic, and trends

The most searched queries on Google related to “prompt” concern meaning, examples, optimization techniques, prompts for ChatGPT, and how to write an effective prompt. Continued interest in these topics has led to a saturation of SERPs with encyclopedic content, practical examples, and how-to guides.

Thanks to SEOZoom’s Question Explorer analysis, we can see that in the “People Also Ask” box associated with the keyword “prompt,” there are over 60 variations of frequently asked questions. This suggests that the public does not associate a shared meaning with the word—but interprets it in different ways, depending on whether it refers to AI, traditional computing, education, speech therapy, or English linguistics.

The type of prompt typed by users also reflects the application domain:

• Educational: “Explain how… works,” “Summarize the text…”
• Technical: “Write a Python function that…” / “Create an SQL command for…”
• Creative: “Generate a story about…” / “Write a dialogue between…”
• Operational or copywriting domain: “Write a headline for…” / “Create promotional text for…”

Usage data shows that many conversations start with simple, often poorly worded prompts and only improve after one or more iterations. The entry into workflows and productivity software (Office, Notion AI, Slack GPT…) further expands the variety and complexity of everyday prompts.

Most common prompts, most used prompts

An aggregate study of OpenAI APIs (February 2025) indicates that the most frequent prompts fall into these categories:

• Requests for explanation (“Explain like I’m five…”)
• Transformations (“Rewrite this text in a more…”, “Convert to tabular format”)
• Composition (“Generate an email/pitch/SEO paragraph…”)
• Contextual translations (“Translate into formal/informal tone…”)
• Correction prompts (“Identify errors in this data”)

Prompts structured in custom micro-frameworks are also on the rise, especially in work environments that use tools such as prompt managers, prompt library extensions, and specialized GPTs. This reflects a maturation in usage: prompt writing is evolving from simple interaction to a repeatable operational module integrated into workflows.

Prompt sharing and documentation platforms—such as PromptingGuide.ai, PromptHero, or GitHub—are facilitating the circulation of increasingly sophisticated templates that can adapt to the needs of multimodal, multi-turn, data-driven prompts.

At this level, the prompt is no longer just input: it is structure, configuration, and predetermined behavior. The numbers confirm this. And users now treat it as such.

Frequently asked questions about prompts (and a useful summary to get you started)

Truly understanding what a prompt is and how to write an effective one requires practice, observation, and awareness. But to get started—or to clarify recurring doubts along the way—simple answers to direct questions are often enough. In the concluding section of this article, we offer a collection of FAQs that summarize what many people are looking for when they approach the world of prompting. From basic definitions to operational strategies, from best practices to mistakes to avoid, this concise guide can be read independently or used to reinforce what has been covered in the previous sections.

When working with generative artificial intelligence on a daily basis, knowing what to ask—and how—is one of the most important skills to develop. Prompts are at the center of interaction and increasingly at the forefront of design. Collections such as this one allow you to train precision, avoid misunderstandings, and achieve useful results.

1. What is a prompt?

A prompt is a textual input provided to a generative artificial intelligence model that guides the generation of output. It can be an instruction, a question, an example, or a structured combination of multiple elements. The model responds based on what the prompt communicates—not on what the user intends to imply.

2. What is a prompt used for?

It is used to tell AI what we want it to do. It can be used to obtain information, create content, generate code, write a summary, analyze data, translate a text, produce a list, or develop ideas. The clarity of the prompt determines the relevance of the response.

3. What is the difference between a generic prompt and an effective one?

A generic prompt is vague, lacking context, and open to arbitrary interpretations. An effective prompt is precise, contextualized, and clearly directs the expected output in terms of format, tone, and content.

4. What are the main prompting techniques?

The most commonly used today are:

• Zero-shot: giving instructions without examples.
• Few-shot: including guiding examples in the prompt.
• Chain-of-thought: guiding the model to construct a step-by-step reasoning process.

5. What does prompt engineering mean?

It is the advanced design of prompt systems intended for repetitive, integrated, or scalable tasks. It includes documenting instructions, standardizing sequences, and predicting output in work or development environments.

6. What is prompt design and how does it differ from prompt engineering?

Prompt design has a user experience (UX) focus: prompts are built to guide AI behavior in specific contexts, such as software, apps, or chatbots. Prompt engineering works more deeply on the process, systematizing and standardizing interactions.

7. How do you write a good prompt?

Start with a clear goal, define the tone and audience, indicate the type of response expected, limit ambiguity, provide only relevant information, and, if necessary, break the task down into several steps.

8. Is it better to use a short or long prompt?

It depends. Too much summarization generates generic responses. Too much text, if not clearly organized, produces inconsistencies. A good prompt is long enough to include all the useful elements, but without overloading the model with contradictory or disordered instructions.

9. What distinguishes a declarative prompt from one with an assigned role?

A declarative prompt gives an instruction (“write an essay on…”), while a role-based prompt guides the style and voice of the output (“you are a university professor who is an expert in…”). Role assignment helps the AI select the right register and content.

10. Can I use the same prompt structure for different tasks?

Yes, if you build a modular structure. Frameworks such as RACE and RISEN allow you to adapt the prompt skeleton to different purposes by modifying only the details (tone of voice, format, required content).

11. What are the most common errors in prompts?

Semantic ambiguity, lack of constraints, information overload, inconsistent requests, implicit expectations, and pressuring the model with out-of-context human requests (“understand what I mean”).

12. Can AI improve a poorly written prompt?

It can attempt to respond, but it does not correct the prompt. If an instruction is vague or contradictory, AI simulates a response based on similar patterns seen in training. The result will often be generic or unrelated.

13. Are there tools for testing prompts?

Platforms such as PromptPerfect, Promptmetheus, OpenAI’s playground, or custom GPT tools offer controlled environments in which to write, test, and optimize prompts with measurable output.

14. Can I use the same prompt on different models?

Partially. Some models (such as Claude, GPT-4o, Gemini) respond well to common structures, but vary in capabilities, accepted tokens, and input format. Complex or multi-turn prompts should be tested individually to avoid inconsistent behavior.

15. Do prompts have technical limitations?

Yes. There are limits in terms of length (tokens), syntax (formatting, punctuation), or type of content accepted. Some models reject sensitive requests, prohibited content, or prompts that circumvent ethical guardrails.

16. How does the use of prompts change in business environments?

They are integrated into workflows, configured as operational modules, interfaced with APIs or CRM systems, and documented for different teams. Here, prompts are strategic resources that fuel automation, internal support, and production efficiency.

17. Are there any ready-to-use prompts?

Yes. Public templates, shared libraries, and collections for specific tasks (SEO, marketing, publishing, development) are available. However, they should always be adapted to your use case to avoid standardized or inappropriate output.

18. What is a prompt list?

A collection of ideas, formulas, instructions, and combinations of prompts that are useful for creating content or directing AI toward certain behaviors. They can be thematic, categorical, or adaptive.

19. Is prompting destined to become a formal skill?

It already is, in part. It is a skill required in marketing, content, UX, customer service, and development. The ability to interact efficiently with generative models is becoming central to work and design contexts that use AI as a co-star.

20. Can I create my own prompting framework?

Yes. It is highly recommended. Defining roles, outputs, steps, tone, and recurring constraints allows you to design a customized flow that is replicable and easily optimized over time.