With the expansion of language-based Artificial Intelligence models, prompt engineering is a discipline that has gained relevance in the technology and business areas. Knowing how to give the right commands to a generative AI model is essential to receive relevant responses that help companies in different contexts.
Follow this reading to understand the concept of prompt engineering, its importance for improving interaction with AI systems, and how the appropriate use of prompts can drive more useful and accurate responses.
What is prompt engineering?
Prompt engineering is the process of generating prompts, or commands, for a language-based AI model, such as OpenAI’s ChatGPT . This discipline focuses on formulating appropriate commands to optimize responses and interaction with the AI model. In other words, it aims to generate relevant and useful results for a given question.
How important is prompt engineering ?
The field of prompt engineering is very important to take advantage of the potential of generative AI in various segments of a business. With the advancement in language-based AI models, this area has become popular, mainly because it supports the creation of more meaningful and accurate interactions with models.
To explore the potential of generative AI models and get answers that are close to what you’re looking for, you need to ask the right questions. By doing so, you can reap benefits such as:
Optimization of response efficiency;
Personalization and adaptation to the user's search needs.
For example, imagine you want to know a cake recipe, but you only have a few ingredients available. However, you simply type the command “provide a cake recipe.” The template will provide a generalized answer for a cake recipe, which doesn’t fit your current needs. What can you do to improve this prompt? Providing more details about your needs can help you get more complete and personalized answers.
Let’s say you reworded the prompt by adding the information “prov how to use a shareholder database ide a cake recipe that doesn’t require eggs” . The model will now likely offer alternatives tailored to your needs. So the more well-formulated, descriptive, and personalized your prompts are, the more effective the model’s responses will be.
Applications of prompt engineering
The applications of prompt engineering are vast and can significantly optimize various segments of a business, such as in the areas of:
Customer service;
Marketing;
Data analysis ;
Software development.
Customer service
Using virtual assistant chatbots , companies can streamline the customer service process by creating prompts that interpret user intent across language variations.
By understanding the consumer's needs, the model can find the best solution to meet the demand in question. If well constructed, it can lead to better results in the area of customer service, generating loyalty and greater customer satisfaction.
Marketing
Creating prompts can also significantly improve the marketing area by supporting the generation of content in different formats. These prompts can be adapted to the company's communication objectives to optimize content marketing strategies , with the review and monitoring of professionals in the area.
Data analysis
With the right prompts, a data professional can train AI models to support a business’s data analysis process , extracting valuable insights from large volumes of data and bringing efficiency to the process. When well-formulated, prompts can contribute to data exploration, cleaning, visualization , and reporting , as well as predicting trends in data sets.
Software development
The application of prompt engineering in software development is also promising, contributing significantly to a developer 's work by assisting in processes such as:
Code generation;
Code refactoring;
Performance improvement;
Troubleshooting and bug fixing, etc.
Prompt Engineering Basics
For a generative model like ChatGPT to respond appropriately to prompts, some factors need to be considered to increase the level of reliability of the system's responses.
OpenAI, the company that helped popularize the use of generative AI models like ChatGPT, shared some guidelines to make creating complex prompts more efficient, such as:
Provide clear instructions;
Break down a complex task into substeps;
Ask the model to justify their answers and then summarize them;
Ask the model to provide explanations before responding;
Generate multiple responses and then have the model choose the best option, etc.
Other educational resources, such as the Prompting Engineering Guide , also reinforce some best practices for prompt design, such as:
Start by simplifying the commands;
Provide clear instructions for the models;
Ensure maximum specificity in prompts by offering details and examples;
Avoid vagueness and general commands. Instead of saying “name some Bruno Mars songs”, say “name the five most famous songs by the singer Bruno Mars”.
Opt for commands that tell the model what to do , rather than what not to do, etc.
Techniques for creating effective prompts
Prompt engineering techniques are essential to ensure that more precise and effective questions are formulated, generating better answers for each question. Some commonly used techniques are:
Zero-shot Prompting
Few-shot Prompting
Chain-of-Thought Prompting
Self-consistency
Tree of Thought
Zero-shot Prompting
Zero - shot prompting involves giving a command to the language model to generate a useful response without adding examples or specific context. This type of prompting is very useful for understanding the model's ability to generate responses based solely on its previous training.
An example of this type of prompt would be: “describe the water cycle”. In this case, there is no information or examples to help formulate a more precise and detailed answer.
Few-shot Prompting
Unlike Zero-shot Prompting , Few-shot Prompting provides multiple examples in the prompt to help the model formulate a response that is closer to the expected result. Instead of simply creating a question, the prompt engineer will provide a few example responses to help the model understand the structure and context of the question.
For example, when asking the AI to write a text in the writing style of Clarice Lispector, provide some examples of various poems by the writer to give the model some direction.
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