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Using Descriptive Prompts for Complex Tasks
When a problem is too complex to be effectively defined using a structured, attribute-based format, it is often better to instruct a Large Language Model with a clear and detailed natural language description of the 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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Improving AI-Generated Ad Copy
A marketing team is using a Large Language Model to brainstorm a novel marketing campaign for a new brand of ethically-sourced coffee. Their goal is a campaign that is both highly creative and emotionally impactful. They use the following structured prompt:
TASK: Generate Marketing Campaign; PRODUCT: Coffee; ATTRIBUTES: [ethically-sourced]; TONE: [creative, emotionally impactful]. Which of the following statements best evaluates why this prompt is likely to be suboptimal for this specific goal?Evaluating Prompts for a Complex Business Task