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Name:Content Prompt Formatting Style
The 'name:content' style is a popular formatting method where each input or output in a prompt is labeled with a descriptive name, creating a clear key-value structure. This approach is highly versatile for defining tasks for a language model. It can be applied to simple interactions, such as structuring a conversation or a question-answering session, and also extends to describing more complex operations, like providing a detailed specification for a machine translation task.
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Ch.3 Prompting - Foundations of Large Language Models
Foundations of Large Language Models
Foundations of Large Language Models Course
Computing Sciences
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Example of a Code-like Prompt for Machine Translation
Example of a Generic Code-like Prompt Template
Name:Content Prompt Formatting Style
Example of a Demonstration in a Code-like Prompt
A developer needs a large language model to perform two tasks on a given text: create a one-sentence summary and extract the names of any people mentioned. Below are two potential prompt structures for this task.
Structure A:
Summarize the following text in one sentence and list the names of any people mentioned. Text: {input_text}Structure B:
[INPUT_TEXT] = "{input_text}" [TASK_1] = Create a one-sentence summary. [TASK_2] = Extract all names of people. [OUTPUT] summary: people:Which of the following statements best analyzes why Structure B is a more effective prompt design for ensuring reliable and consistent results?
Improving Prompt Reliability for Information Extraction
Using Descriptive Prompts for Complex Tasks
Critiquing a Multi-Task Prompt
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Example of a Name:Content Prompt for Conversation
Example of a Name:Content Prompt for Question-Answering
Example of a Code-like Prompt for Machine Translation
Storing Prompt Data in Key-Value Formats like JSON
Example of a Code-Style Demonstration for English-to-German Translation
Examples of Key-Value Pairs in a Code-Style Translation Prompt
A developer is building a system to extract structured information from customer support emails. Their initial prompt, 'From the following email, extract the customer's name, order number, and the core issue,' often results in the language model producing a long, narrative paragraph that mixes the requested information with other details. Which of the following best explains why adopting a
name:contentformatting style, where the model is prompted to complete fields likecustomer_name:,order_number:, andissue_summary:, would be a more effective strategy?Designing a Structured Prompt for Recipe Generation
A developer is creating a few-shot prompt to teach a language model how to convert informal user requests into a structured JSON object for an internal API. The goal is to extract the user's name and the item they want to order. Which of the following prompts most effectively uses a clear, key-value formatting style to structure the task and guide the model toward a reliable output?
Example of a Name:Content Prompt for Task Specification