In 2024, summarizing large texts remains a challenge for many users. However, there are better ways to achieve this, and this guide will show you exactly how. We'll cover one improved summarization prompt and an advanced technique from a recent research paper that most haven't explored. Additionally, due to popular demand, I'll be sharing weekly prompting tutorials, focusing on specific techniques and how to use them effectively with large language models like GPT-4, though these methods work with any large language model you choose.
We'll start with a simple, yet effective, technique using a Wikipedia article. For this exercise, we'll summarize the article on Brazil. The most basic way to do this is by copying all the text from the Wikipedia article and pasting it into the large language model (LLM). Most users simply use the prompt “summarize this,” but I recommend an enhanced prompt for better results.
Here’s a more effective prompt structure:
You are a [role] summarizing for a [target audience]. Summarize the text given to you in five bullet points.
You’ll replace [role]
and [target audience]
with specifics, such as "YouTube Creator focused on history videos.” This personalization helps generate more relevant summaries.
Example:
You are a YouTube Creator focused on history videos. Summarize the text given to you in five bullet points.
The advanced technique involves using additional "building blocks" in the prompt. Here are the four optional elements you can add:
Here’s how a prompt looks with all building blocks:
You are a YouTube Creator focused on history videos. Summarize the text given to you in five bullet points, with a tone suitable for an 11-year-old, focusing on the 16th century, in 100 words, include direct quotes where applicable.
A 2023 research paper introduced a technique called GPT-4 Summarization with Chain of Density Prompting. This method starts with a summary, then iteratively compresses and densifies it to create increasingly concise versions.
To use it:
There are several ways to input your text for summarization besides copy-pasting:
To conveniently reuse the chain of density prompt, you can:
Q: What is the enhanced summarization prompt?
A: It includes specifying a role and target audience, and often the length or number of bullet points for the summary.
Q: What are the building blocks for a detailed prompt?
A: Tone, goal, length, and direct quotes.
Q: What is Chain of Density Prompting?
A: A technique that iteratively compresses and densifies summaries for maximum information in minimal space.
Q: How can I input different types of text into the model?
A: Through URL links, uploading files, uploading images, or using drive integration.
Q: How do I organize my commonly used prompts?
A: Use a prompt organizer or convert the prompts into a reusable GPT.
Feel free to explore other practical AI skills and techniques to enhance your productivity.
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