Speak AI’s Language: The Art of Prompt Engineering

The simplest definition of Prompt Engineering is how humans can communicate with artificial intelligence. It is the process of telling AI what we want and how we want it. Input information can be in the form of a text message, a set of keywords, or other forms of guidance.

Prompting refers to the process of providing input (or guidance) to a language model like ChatGPT in order to obtain the best output. It is a technique for controlling the output in the form of specific content for us. For example, we may want an image exactly the way we talk to an artist and expert. We describe what we want with the best words and even a preliminary design for them to lead to the best and most relevant output, which is the image we want. We may need to specify the name of an artist, artwork, painting style, and similar items in the input. In addition, some technical terms may need to be inserted during input or the prompt event.

A prompt engineer translates your idea into words that artificial intelligence can understand (it can be seen as a translator between human language and AI language). The goal of prompt engineering is to find a command that gets the best results from artificial intelligence. The prompt engineer’s aim is to optimize phrases and queries and to provide the best description as input data for artificial intelligence algorithms, leading to the desired results.

Just as a specialist uses better phrases and techniques when searching on Google compared to an ordinary person, resulting in more specific and accurate results, this is what prompt engineering does for artificial intelligence. It helps to obtain the best results by providing appropriate input and guidance to AI algorithms.

Principles of Prompting:

1. Familiarity with the language model (such as GPT chat) that we are using. This includes understanding its capabilities, limitations, and the types of prompts it can respond to.
2. Trial and error of different types of prompts to test how the model responds to them.
3. Understanding the context and concept: to create accurate and appropriate responses, it is important to understand the framework in which the generated output will be used, including understanding the audience, purpose, and any specific requirements or limitations.
4. Fine-tuning the model: training the model using specific commands.
5. Evaluation and monitoring: to ensure that the model creates accurate and appropriate responses, evaluating the generated text and monitoring the model’s performance over time is important.
6. Continuous learning, evaluation, and adaptation: as the model is used, continued monitoring and fine-tuning are important. This can include providing new data, adjusting parameters, or retraining the model for new tasks.

General components of a prompt:

• Seed: A piece of text that is relevant to the desired topic or task (a general description of the topic).
• Conditional statements: Specifying certain conditions or requirements (e.g., the content should be within 300 characters).
• Special keywords: Keywords that are relevant to the desired output.
• Control codes: Codes that can be used to adjust the length and format of the generated text or to determine that the text should be written in a specific style.
• Special prompts for precise adjustment: For example, using prompts related to the desired topic, in other words, calibrating.
• Special formatting: Using numbered lists can help the model produce structured text in a more organized format.
• Special persons: Characteristics such as age, gender, occupation, etc. that we can use to guide the model to produce text consistent with a particular character or specific individuals.
• Special language: For example, if you want to generate text in French, you can add the word “French” to the prompt.

By creating precise commands that are specific to a particular task or topic, we can produce more accurate, useful, and relevant outputs.

Prompt engineering is considered a must in the modern