Write 22 Blog Posts in 9 Minutes? «No Problem» says GPT-3 ⏱️

TL;DR

GPT-3 can automate blog-post drafting through a Python script that takes titles from a topical map and generates structured content. The creator supplied topics for 22 posts, ran the script from the terminal, and received drafts containing title suggestions, introductions, paragraphs, and conclusions. However, every draft still requires fact-checking and editing because the output may be false, repetitive, or unhelpful. Read on for the workflow and safeguards.

Transcript

today I wanted to test the speed of how fast gpt3 can write blog posts so I created a script where the only thing I had to do was put in the title of the blog post and the rest was automated I don't know about the quality of the text output but I left a link in the description so you can go read for yourself anyways let's see how I did this first I... Read More

Key Insights

• 🏣 GPT-3 can automate the process of writing blog posts by generating content based on provided input.
• 🧘 The generated blog posts require careful review and editing to ensure accuracy and avoid repetitive or false content.
• 🍁 A topical map can help define the structure and content of the blog posts.
• 🐎 The script performed well in the speed test, producing blog posts with proper formatting and quality.
• 🏃 Caution should be exercised when using AI-generated content for publishing purposes.
• 🌱 The creator plans to start a membership and provide step-by-step tutorials on creating Python scripts with GPT-3 and other APIs.
• 🛫 They also have plans related to generative AI and AI art.

Questions & Answers

Q: How did the creator use GPT-3 to write blog posts quickly?

The creator built a Python script that required only the blog-post titles as input. After the titles were entered into the main-topic variable and the script was started from the terminal, the remaining drafting process was automated.

Q: What input did the GPT-3 blog-writing script require?

The script used a list of topics that also served as H1 titles. The creator placed everything they wanted to cover into the main-topic variable before running the script.

Q: How was the topical map used to generate the articles?

The creator first made a topical map covering the AI subjects they wanted to write about. Its 22 entries became the topics the script used to create separate blog posts.

Q: What structure did the generated blog posts include?

One example contained five H1 title suggestions, an introduction, several sections about AI in healthcare, and a conclusion. The script could also produce extra introductions and conclusions when those elements were missing.

Q: How long were the AI-generated blog posts?

The creator described them as standard blog posts of around five to eight hundred words, possibly a little longer. They were intentionally kept from becoming too long and were formatted for easy reading.

Q: Were all of the generated posts uploaded to the test website?

No. The creator said only some posts were uploaded as a test to fill out the homepage, rather than uploading every generated article.

Q: Can GPT-3-generated blog posts be published without editing?

The creator advises against publishing unedited AI content. Each post should be reviewed for factual accuracy, edited, and made original because generated content can be false, repetitive, or unhelpful.

Q: What additional elements were added to the published test posts?

The creator added images made with Midjourney to the articles on the test website, superstrongai.com. They were pleased with the images and their consistency across the homepage.

Summary & Key Takeaways

• The creator used GPT-3 to automate blog post writing by providing a title and letting the script generate the content.

• They started by creating a topical map of the AI topic they wanted to write about.

• After running the script, they were able to generate blog posts with H1 titles, paragraphs, introductions, conclusions, and included images.