Prompt Engineering: definition, examples and good practices

Prompt Engineering: definition, examples and good practices

Prompt engineering, also known as rapid or instruction engineering, includes various techniques and methods to optimize the prompts of generative AI tools. Discover in more detail the meaning of the term, take a look at some examples and access the list of best practices.

When working with AI tools, it is essential to write instructions appropriately. In this way, you can take full advantage of the potential of language models and achieve high-quality results, whether in text, images or videos. As artificial intelligence has evolved, the demand for professionals trained to use it effectively has also increased. In fact, a new profession has emerged, that of the prompt engineer .

Prompt engineering: definition

The term “prompt engineering” refers to those techniques and methods used to optimize the formulation of prompts in natural language processing (NLP) and large language models (LLM) such as GPT. -3 or GPT-4, based on machine learning . The goal is to obtain higher quality, more precise or specific answers, since the way in which a question or instruction is posed has a great impact on the quality and relevance of the answer provided by an artificial intelligence .

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Optimizing the instructions given to AI models requires creativity and precision, as well as in-depth knowledge of the linguistic model . The choice of words and their order can alter the result offered by the AI. Instructions may contain natural language text, images, or other types of data. It is important to note that the same question can show different results depending on the AI ​​platform being used, which highlights the need to adapt the instructions to each artificial intelligence text generator or AI video generator .

Why is rapid engineering important for AI?

Instructional engineering is essential to getting the most out of language models and thus achieving better results with Generative AI tools . For example, a prompt engineer can try to formulate the same question in different ways to evaluate how each of the formulations influences the result provided by the AI. With tools like ChatGPT, word order and single or multiple use of a modifier, such as “very,” can significantly affect the result.

In image AI tools , rapid engineering can help fine-tune the characteristics of the generated images. These features often provide the ability to create AI images in a specific style, perspective, aspect ratio, or image resolution. The first instruction usually serves only as a starting point. Subsequent prompts can be used, for example, to soften or strengthen certain elements and add or remove objects from an image.

Even in the process of creating new tools, Prompt Engineering can be useful in fine-tuning LLMs and improving work processes to achieve specific results. In addition, prompt engineering is important for AI for another series of reasons:

  • Optimize results : Well-planned prompt engineering can lead to the generation of more accurate and relevant results from language models.
  • Efficiency : Well-formulated prompts allow a model to deliver the desired information more quickly, without the need for multiple prompts or additional iterations.
  • Controlling results : By properly designing prompts, the user can direct various aspects of the AI’s response, such as its length, style, and tone.
  • Reducing errors – With clear and concise instructions, potential biases, misunderstandings, or inaccurate responses from a model can be minimized.
  • Advanced uses : With proper prompt formulation, AI models can be used for specific tasks or in specialized fields for which they were not initially designed.
  • Experimental insights : Experimenting with different instructions can help you better understand how a particular generative AI works and how it responds to different inputs.

Prompt engineering: examples

The prompts that can be used to create text, images or videos differ significantly from each other. However, something that all artificial intelligence programs share is that the proper formulation of prompts allows users to interact more effectively with the AI ​​tool they are using to get answers that meet their expectations.

Examples of instructions for text generators

In the text generator prompts you can take into account:

  1. Precision
  • Original Prompt: “Tell me about the trees”
  • Improved prompt: “Explain to me the process of photosynthesis in deciduous trees”
  1. Response format
  • Original prompt: “What are the benefits of solar energy?”
  • Improved prompt: “Name five benefits of solar energy”
  1. Include examples to guide the answer
  • Original prompt: “Write a sentence about Paris”
  • Improved Prompt: “Write a sentence about Paris in the style of Hemingway”
  1. Extension and details
  • Original prompt: “Describe the water”
  • Improved prompt: “Write a detailed scientific explanation of the molecular structure of water”
  1. Avoid prejudices
  • Original Prompt: “What do you think about cryptocurrencies?”
  • Improved Prompt: “Describe cryptocurrencies in a neutral and objective way”
  1. Context
  • Original Prompt: “Why Are Stocks Falling?”
  • Improved Prompt: “Given economic factors, why might tech stocks fall in a recession?”
  1. Writing style and perspective
  • Original prompt: “Tell me the story of Napoleon”
  • Improved prompt: “Tell me the story of Napoleon from the perspective of one of his soldiers.”

Also Read: AI for images: the best AI image generators

Examples of prompts for image generators

Prompt engineering is not only relevant for language models, but also for models that generate images, such as DALL-E. In the case of image generators, the instructions must describe verbatim the type of image to be generated:

  1. Precision
  • Original Prompt: “Cat”
  • Improved Prompt: “Brown cat sleeping on blue pillow”
  1. Combination of elements
  • Original prompt: “Buildings and clouds”
  • Improved Prompt: “An old Victorian house resting on floating clouds”
  1. Style and era
  • Original Prompt: “Cars”
  • Improved Prompt: “Futuristic 1950s Retro Style Cars”
  1. Feelings and atmosphere
  • Original Prompt: “Forest”
  • Improved Prompt: “A dark, misty forest in the moonlight”
  1. Combination of unusual elements
  • Original prompt: “Table and fruit”
  • Improved prompt: “A table made of watermelons with a silver tray with slices of dried banana”
  1. Perspective and dimension
  • Original Prompt: “Mountains”
  • Improved Prompt: “A giant mountain shaped like an overturned tea glass”
  1. Abstraction
  • Original Prompt: “Emotions”
  • Improved Prompt: “Representation of joy as a bright explosion of color”

Examples for video generators

For video generators, the challenge of prompt engineering consists of trying to transmit as precisely as possible the characteristics that the dynamic and temporal sequence of actions and events that are going to be generated must have. A good prompt helps specify what sequence of actions is desired, in what environment they take place, for how long, and what interactions occur:

  1. Sequence of actions
  • Original Prompt: “Running Cat”
  • Improved Prompt: “Brown cat slowly walks past a puddle and then jumps into it”
  1. Environment and tone
  • Original Prompt: “Beach Scene”
  • Improved Prompt: “A lonely beach at sunset, with the waves breaking wildly and a group of seagulls flying on the horizon”
  1. Temporal evolution
  • Original Prompt: “A growing flower”
  • Improved Prompt: “Process of a rose transforming from a closed bud into a flower in just 30 seconds”
  1. Dynamic actions
  • Original prompt: “Sports match”
  • Improved Prompt: “Basketball game in which a player makes a crucial three-point shot in the final seconds”
  1. Combination of elements and transitions
  • Original prompt: “Hours of the day”
  • Improved Prompt: “Panoramic view of a city showing the transition from day to night in a span of 30 seconds, with the city lights gradually turning on as night falls”
  1. History and narration
  • Original Prompt: “A bird flies”
  • Improved Prompt: “A young bird trying to fly for the first time ends up conquering the skies after a few failed attempts. “He manages to return safely to his nest.”

Best practices for prompt engineering

By formulating the prompts correctly, the results that can be obtained from generative AI tools, especially language models, will be as expected. There are some best practices that should be considered when formulating instructions:

  • Conciseness : Clarity in the wording of an instruction helps the AI ​​better understand what is expected of it.
  • Precision – Make sure you have phrased your instructions to the level of detail necessary to get the type of response you want.
  • Experimentation : If you don’t immediately get the desired answer, try phrasing the question differently or including more context.
  • Formatting Instructions : If you want your response to have a certain format (for example, list, short paragraph, formal language, specific length, etc.), you must specify it.
  • Inclusion of examples to guide the response : including an example of the response you want to obtain can serve as guidance for the AI ​​when developing the result.
  • Context : Before responding to a given prompt, some AIs find it useful to have additional information or a broader context.
  • Avoid ambiguity – Avoid unclear or ambiguous terms.
  • Limit and direct : If you are concerned that the AI ​​may respond in a biased way or if you want a specific style or perspective, provide unambiguous instructions.
  • Review : It is important that you review the AI’s responses and make sure they are accurate.
  • Iterative approach – It can often be helpful to take an iterative approach and refine the question based on the answers received.

Also Read: Prompt Engineering: Everything You Need To Know

What profile should a prompt engineer have?

The prompt engineering profession offers promising opportunities to those with a deep understanding of language processing and a creative mindset . As AI and NLP technologies are introduced in different sectors, the demand for this profile also grows.

Although there are no specific training requirements, having a degree in a related field can be helpful. For example, although programming knowledge is not essential, a degree in computer science or linguistics can make it easier to understand language models and develop prompts. Prompt engineering consists mainly of understanding how the language works so that a prompt can be designed to achieve the desired results. For this, the following knowledge may be useful:

  • Understanding AI and machine learning : understand how neural networks work , particularly language models, to better understand the mechanisms that generate results.
  • Analytical thinking – to be able to analyze the results and change instructions accordingly.
  • Communication skills : to formulate clear and concise instructions.
  • Error detection : to be able to detect inaccuracies or errors in the responses of an AI model and make the necessary changes.
  • Domain-specific knowledge : Depending on the domain in which it is applied, specific knowledge may be required to effectively design and evaluate prompts and responses.
  • Continuous learning : artificial intelligence and machine learning are evolving at a significant speed. Therefore, good prompt engineering requires a commitment to continuous learning and the willingness to constantly adapt to new technologies.
  • Teamwork – Often, a prompt engineer needs to work with other professionals, such as data scientists, software engineers, or business analysts.

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