Are you using artificial intelligence, but often getting vague or off-topic results? It’s frustrating to waste time retyping your queries.
The issue isn’t the tool itself—it’s your prompt. The prompt is the key that allows the AI model to understand and generate exactly what you want. A poorly formulated prompt wastes the AI’s potential.

Imagine getting precise answers, perfect code, or stunning images on the very first try. That’s the power of well-mastered prompt engineering.

In this comprehensive guide, we define what a prompt is and reveal concrete strategies (based on Google documentation) to optimize your queries and achieve outstanding results. Discover how to communicate effectively with AI…

What is a prompt in AI?

In artificial intelligence (AI), a prompt refers to the request or instruction in natural language that you provide to an AI model to obtain a specific response.

Simply put, it’s the input text submitted to the model (for example, a large language model) to trigger content generation.

Prompts can take many forms: a simple question, a keyword, a detailed instruction, a snippet of code, or even a creative text example to continue.
This initial request forms the foundation of what the AI will produce.

A prompt serves as a roadmap to guide the model toward the desired output. Prompts are sometimes called “instructions” or “queries” addressed to the AI.
The clearer and better structured the prompt (e.g., specifying context or expected style), the more precisely the AI understands the task. An effective query improves the relevance and quality of the generated response, while a poorly defined prompt can lead to off-topic or inaccurate results.

In short, a prompt is the starting point for any interaction with a generative model. It allows a user to “communicate” their intention (ask a question, give instructions, provide a text start, etc.) and receive content (text, image, code) in response. This concept is fundamental in the field of generative AI.

The role of the prompt in AI model behavior

The prompt plays a central role in an AI model’s behavior.
It can be seen as a “roadmap” that guides the model’s output toward the desired result.

In practice, prompt engineering involves designing and refining these queries to guide the model to generate the expected response. The better structured the prompt (clear instructions, defined context), the more relevant and accurate the response will be. Conversely, a vague or poorly formulated prompt often produces off-topic or imprecise results.

To achieve better outcomes, several aspects of a prompt can be optimized:

• Prompt format: The form and style of the query influence how the model interprets it. For example, a model may respond better to a naturally phrased question, a direct command, or a structured format depending on its internal capabilities.
• Context and examples: Providing relevant context or examples in the prompt helps the model understand the task. Specifying tone, theme, or giving input/output examples can lead to more precise results.
• Fine-tuning and adjustment: Models can be refined for specific tasks using dedicated prompts (fine-tuning), and prompts can be adapted based on feedback. These incremental adjustments improve model performance for the target task.
• Multi-turn dialogue: In multi-step interactions, including the conversation history in the prompt allows the model to maintain coherence and context between responses.

It’s worth noting that some very recent models (e.g., Google’s Gemini series) often deliver good results even with relatively simple prompts for common tasks. However, for complex or highly specific tasks, careful prompt design and optimization remain essential to achieve optimal results.

The prompt processing workflow for content generation

Once a prompt is submitted, the AI model analyzes it to determine the response to generate. The prompt serves as the initial input: the model interprets it to extract the task to be performed.

According to Vertex AI documentation (Google), the response format depends on the model type: a text generation model, for example, will complete or continue the text provided in the prompt, while an image model will create a visual based on the prompt description.

Response generation typically occurs progressively: the model produces content step by step, each new fragment building on what has already been generated and the prompt context. This process continues until the model determines it has fulfilled the initial request.

In short, the prompt triggers the model’s internal mechanism to produce the final requested content (text, code, image, etc.) in accordance with the initial instructions.

What are the different types of prompts?

Prompts can be categorized based on the type of content they aim to generate.
The main categories are textual prompts, visual prompts, and multimedia prompts.

Modern AI models can handle these different modalities: according to Vertex AI documentation, a prompt can lead to the generation of text, images, videos, music, etc., depending on the model used.

Textual prompts

Textual prompts are written entirely in natural language. They lead to the generation of textual content by the AI.

Google documentation provides many examples, such as requests to:

• Write a story (“Write a short story about a young woman who discovers a magical portal in her attic.”)
• Summarize a text (“Summarize the main points of the following article on climate change.”)
• Translate text from one language to another (“Translate the following text from French to Spanish…”)

In each case, the textual prompt often specifies style, tone, or format to guide the AI toward the appropriate response.

Textual prompts can also be used for chatbot interactions: for example, you can define a role and a starting scenario (“You are a friendly chatbot helping users solve their problems…”) to obtain context-aware responses.

Visual prompts

Visual prompts relate to generating or modifying images.

The request can include a detailed visual description or a reference image. For example, a prompt could ask for a photorealistic image of a sunset:
(“Photorealistic image of a sunset over the ocean, with palm trees…”)

Artistic creation can also be guided by specifying a style:
(“Create an impressionist painting of a busy street in the rain…”)

Some visual prompts include an existing image as input and request edits—for example, providing a photo and asking “replace the background with a starry night sky” or “remove the person in the photo and replace them with a cat.”

These examples show that a visual prompt precisely describes the desired elements or modifications so the model can generate or transform an image consistent with the request.

Multimedia prompts

Multimedia (or multimodal) prompts combine multiple types of data in a single query.

Modern models (like GPT) can handle multiple modalities.

For example, a multimodal prompt could combine text and an image to guide the model. In this context, the AI can generate a response that blends text with visual or audio elements.

Thus, a “multimodal query” leverages the model’s capabilities across multiple formats simultaneously, allowing the prompt to result in diverse content (text, image, video, audio…).

Examples of AI Prompts According to Objectives

AI prompts can be tailored to different goals (text, Q&A, code, images, etc.).
Here are some examples of common use cases and associated prompts:

Language and Text Generation

• Creative Writing: Specify the genre, tone, style, and narrative elements to guide the AI in creating a text (story, narrative, etc.). For example: “Write a short story about a young woman who discovers a magical portal in her attic.”
• Text Summarization: Provide content to the AI and ask it to generate a concise summary of the main points. For example: “Summarize the key points of the following article on climate change.”
• Translation: Specify the source and target languages to obtain an accurate translation. Example: “Translate the following text from English to Spanish: ‘The quick brown fox jumps over the lazy dog.’”
• Dialogue (Conversation): Assign a role to the model (assistant, chatbot, etc.) to generate context-aware responses. Example: “You are a friendly assistant helping to solve computer problems. The user says: ‘My computer won’t turn on.’ How do you respond?”

Question-and-Answer Systems

• Open-ended Questions: Encourage the AI to provide detailed explanations on a given topic. Example: “Explain the concept of quantum computing and its potential impact on the future of technology.”
• Specific Questions: Target precise information so the AI can answer accurately based on its knowledge or provided context. Example: “What is the capital of France?” or “Based on the provided text, what are the main causes of deforestation?”
• Multiple-choice Questions: Present multiple options for the AI to select the correct answer. Example: “Who wrote the Harry Potter series? A) J.R.R. Tolkien B) J.K. Rowling C) Stephen King”
• Hypothetical Questions: Propose imaginary scenarios for the AI to reason about and consider possible outcomes. Example: “What would happen if humanity could travel at the speed of light?”
• Opinion Questions: Invite the AI to express a reasoned point of view on a topic. Example: “Do you think artificial intelligence will ever surpass human intelligence? Why?”

Code Generation

• Code Completion: Provide an incomplete code snippet and ask the AI to complete it considering context and programming language. Example: “Write a Python function to calculate the factorial of a given number.”
• Code Translation: Specify a source and target language for the AI to convert a code fragment. Example: “Translate the following code from Python to JavaScript: def greet(name): print(‘Hello,’, name)”
• Code Optimization: Ask the AI to analyze existing code and suggest improvements for efficiency or readability. Example: “Optimize the following Python code to reduce execution time.”
• Code Debugging: Submit code containing errors and ask the AI to identify issues and suggest fixes. Example: “Debug the following Java code and explain why it throws a NullPointerException.”

Image Generation

• Photorealistic Images: Describe the scene in detail (objects, environment, lighting, style) to obtain realistic, high-quality images. Example: “A photorealistic image of a sunset over the ocean with palm trees silhouetted on the horizon.”
• Artistic Images: Specify artistic style, techniques, and subjects to generate images in a given style (e.g., an impressionist landscape). Example: “An impressionist painting of a busy street in the rain, with pedestrians holding umbrellas.”
• Abstract Images: Encourage the AI to produce conceptual images evoking ideas or emotions through shapes and colors. Example: “An abstract image representing the concept of hope, using bright colors and fluid forms.”
• Image Editing: Provide an existing image and describe desired modifications (removing elements, changing background, etc.) so the AI can retouch or enhance it. Examples: “Change the background of this photo to a starry night with a full moon,” or “Remove the person from this image and replace them with a cat.”

How to Write a Good AI Prompt: Strategies for an Effective and Relevant Prompt

1. Set Clear Goals and Objectives:
   Start by defining exactly what you expect from the AI. Use precise action verbs that indicate the desired task (e.g., “write,” “explain,” “compile”).
   Specify the length or format of the expected output (word count, document type, etc.), and indicate the target audience (youth, experts, general public, etc.).
   These instructions provide the AI with a clear framework: asking for a “500-word text” or a “bullet-point list summarizing…” guides the response precisely.
2. Provide Contextual and General Information:
   Giving context helps the AI better understand the query. Including relevant data or facts in the prompt anchors the response.
   You can also cite specific sources or documents (like a report or excerpt) so the AI draws information from the right place.
   It’s useful to define key terms or concepts (briefly explain unfamiliar concepts) so the AI doesn’t misinterpret them.
   Additionally, indicate a tone or perspective—e.g., ask the model to “act as a domain expert” or an “instructive teacher.” A well-contextualized prompt gives the AI clear reference points and improves response relevance.
3. Use Few-shot Prompts:
   Including examples in your prompt shows the AI exactly what you expect. Provide input-output pairs: first show how you would perform the task (input) and the desired response (output), then ask the model to do the same for a new item.
   For instance, provide two humorous sentences, then two formal ones, and finally ask the AI to write a third sentence in one of these styles.
   These examples indicate tone, structure, and expected level of detail. Presenting two or three prompt-response pairs helps the AI grasp the desired format. Including examples acts as an additional instruction clarifying your request.
4. Be Precise:
   Precision is crucial to avoid misinterpretation.
   Avoid vague language and specify every element of your request.
   For example, instead of “describe something about climate change,” say “write a compelling text advocating for stricter carbon emission regulations.”
   Quantify instructions when possible: asking for a “long poem” is vague, while “a 14-line sonnet exploring love and loss” provides an exact framework.
   For complex tasks, break them into clear sub-steps. Instead of “create a marketing plan,” specify: “1. Identify target audience. 2. Develop key messages. 3. Choose appropriate channels.”
   Explicit precision guides the AI toward the desired outcome. A vague prompt like “a muffin recipe” may miss details (e.g., quantity), whereas a detailed prompt (“I’m hosting 50 guests, provide a recipe for 50 muffins”) produces a suitable response.
5. Iterate and Experiment:
   Rarely is a prompt perfect on the first try.
   Test multiple formulations: use synonyms, change word order, vary sentence structure. Adjust detail and specificity in each version to see how the AI responds.
   Even small additions or removals of information can change the generated output. Experiment with prompt length—shorter or longer instructions can yield different results.
   Try varying personas or styles (e.g., ask the model to respond like an experienced developer, a teacher, or a chef).
   This experimentation phase—tweaking keywords, tone, and structure—helps identify what works best for your use case.
6. Leverage Chain-of-Thought Prompts:
   “Chain-of-thought” prompts encourage the AI to explain its reasoning step by step.
   They guide the model to break down problem-solving into intermediate steps.
   For example, explicitly ask it to “solve this problem step by step” and justify each sub-step.
   You can also have it explain its thought process for reaching a conclusion or provide a sequential checklist (e.g., deciding if an email is spam).
   This method helps the AI “think” more logically: as Google notes, breaking a complex problem into steps improves accuracy and completeness.
   Chain-of-thought prompts give the model a clear framework to reach a satisfactory solution.

Advantages of Prompt Engineering

Improved Model Performance

Carefully crafting a prompt provides the AI model with clear context and instructions.
In return, this leads to more accurate, relevant, and informative results. In practice, a well-designed prompt guides the AI toward the desired response, optimizing the model’s performance.

Reduction of Biased or Potentially Harmful Responses

By refining prompts, you steer the AI to minimize the generation of biased or inappropriate responses. Prompt engineering helps mitigate biases present in models and reduces the risk of offensive or unwanted content. This approach enhances the reliability and safety of the results obtained.

Greater Control and Predictability

Prompt engineering gives better control over a model’s behavior.
By carefully choosing prompts, the engineer can influence the AI’s responses to remain consistent and predictable. This increased mastery helps ensure that the model produces the expected outcomes.

Enhanced User Experience

Clear and concise prompts simplify the interaction between the user and the AI model.
By effectively specifying their expectations in the prompt, users can obtain relevant responses more easily.
Overall, the experience becomes more intuitive and satisfying.

Technical and Ethical Challenges of Using Prompts

Mastering prompt engineering requires understanding the strengths and limitations of AI models.
Each large language model (LLM) has specific capabilities: an unsuitable prompt can lead to incorrect results if the model lacks the required knowledge.
Additionally, these imperfect models may misinterpret insufficiently precise instructions, making it difficult to obtain an exact answer. This technical complexity requires formulating highly precise and contextualized prompts to achieve the desired outcome.

From an ethical perspective, prompt engineering must also address bias. Since AI models are trained on real-world data, they may reflect and amplify the stereotypes contained therein. Without vigilance, a poorly calibrated prompt can generate discriminatory or misleading responses, reinforcing these biases. Prompt engineers must therefore identify and correct such biases during prompt design to minimize negative effects.

Take Action…

You now have all the tools to transform your interactions with AI.
Mastering prompts is the essential skill to fully harness the potential of generative models.
Don’t settle for mediocre results. Immediately experiment with optimization strategies (precision, context, chain-of-thought).
Review your recent unsatisfactory queries and craft a perfect prompt using these guidelines. The results will surprise you.