Traditional natural language processing techniques have proved useful for a long time, but they are complex and require a deep understanding of machine learning. Generative pre-trained language models are showing better results in language generation and meaning extraction and are easier to use. Business leaders, developers, and technologists should explore how to program AI with large language models (LLMs) due to their ease of use and stellar results. Learning how to write prompts is much simpler than all that is involved in becoming a machine learning engineer.
Prompts are directions, written in natural language, and are the starting point for any task you would like the model to perform. Let’s explore large language models, the importance of prompts, and how to write prompts within the Mantium platform.
Large language models (LLMs) use deep learning algorithms and vast text-based datasets to parse text, predict, and generate language. LLMs vastly extend the capabilities of what systems can do with text and are the technology behind a machine’s ability to generate highly fluent language. LLMs work so well because of the amount and type of data they’re trained on. The training data might consist of Wikipedia articles, books, blogs, forums, and other digital sources, depending on the model.
A prompt is a portion of text that contains a description, examples, or patterns of the language task. It guides an LLM to give a favorable and contextual response to solve the language task. Simply put, it tells the LLM what you want it to do. LLMs can handle a wide range of tasks with only a few examples as input. LLMs leverage written task instructions or demonstrations as context while not updating the parameters in the underlying model. These natural language prompts are where the human element and interaction occur. To build an AI application, you will use prompts to complete tasks such as text generation, classification, semantic search, sentiment analysis, and more.
Depending on what problem you are trying to solve, a prompt might be simple instructions or a list of examples. Using OpenAI’s Davinci Instruct engine from GPT-3, for example, you might give instructions as simple as the following:
Rewrite the provided website text:
Your input for this prompt would simply be the text you would like re-worded. The output would be text with the same meaning but with different words or styles.
Using another model, you might simply write out an example or two of what you want to see, like this:
Subject: AI and tourism Blog post ideas: 1. The 10 most important AI innovations for tourism. 2. Forecasting tourism trends with AI. 3. Why tourism needs AI. 4. How AI weather prediction is helping tourism agencies meet demand. ### Subject:
Your input will be a new subject, and the model will output a list of blog post ideas based on that topic. You can see this prompt in action here: Generate a list of blog post ideas.
Mantium offers access to a variety of models and engines within the platform to help ensure the best fit for your needs each time you make a new AI application.
The Manitum platform offers a variety of models to choose from for building out use cases like this. Our developer site offers a suite of tutorials about creating prompts for all of the different models for which we offer access. For example, you can learn how to create a bank request intent classification using OpenAI. This example uses classification to determine customer intent for a customer service workflow.
The Mantium Platform offers an easy, streamlined no-code workflow and can help you build AI-driven process automation. You will experience operational speed and give your organization a competitive edge.
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