In this article, we will explore the different tasks that can be accomplished using prompt engineering and where they can be applied in various applications and industries. We will start by discussing the installation of the OpenAI library and creating a helper function called ‘get completion’ for running prompts.
The first task we will look at is classification, specifically sentiment analysis of product reviews. This is a common use case in the e-commerce industry where businesses want to understand the sentiment of their products to improve decision making and marketing strategies. We will provide an example of classifying a product review into neutral, negative, or positive sentiment.
Next, we will explore few short text classification, which is useful when dealing with difficult output formats ingested by downstream applications. We will use a few short prompt to classify customer support questions into different categories such as car insurance, flood insurance, and irrelevant questions.
Creative writing with AI is another task we will delve into. This is particularly useful in the media industry for generating stories, content, and creative ideas. We will provide an example of creating a short love story with specific constraints.
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llaborative storytelling with AI allows us to brainstorm ideas and complete storytelling based on given context. We will provide an example of completing a short story based on an initial sentence.
Summarization is a task that is highly useful for content creators, readers, and researchers. It saves time and effort by providing quick insights from large articles or research papers. We will demonstrate how to summarize a story into a single sentence and also provide a point-by-point summary.
Translation is another task that can be accomplished using prompt engineering. We will show how to translate a sentence from English to Spanish and also translate multiple sentences into different languages.
Question answering is a task where a model is given a context and specific questions are asked based on that context. We will provide an example of asking questions about an opening ceremony and getting answers from the model.
Conversation AI is a versatile task that finds applications in customer support, content generation, education, virtual assistants, and information retrieval. We will demonstrate how to have a conversation with a bot that answers questions on a specific topic.
Code generation is a powerful task that can automate scripting, prototyping, problem solving, code documentation, data processing, and code translation. We will provide an example of generating code to extract user tags from an input string.
Logical reasoning and reading comprehension involve providing a context and multiple-choice questions to identify the correct answer based on the scenario. This task is useful in educational and employee assessments, research, and decision making. We will provide an example of answering a logical reasoning question based on a given passage.
In conclusion, prompt engineering enables us to accomplish various tasks using large language models. It allows us to classify sentiments, generate creative writing, summarize content, translate languages, have conversations, generate code, and answer questions based on given contexts. By leveraging prompt engineering, we can enhance our applications and improve decision making in various industries.
