• author: WordsAtScale

Creating a Long-Form Article in One Go: A New Method with GPT-3.5


Since the rise of generating text with AI models like GPT-3.5, content creators have been exploring the possibility of creating long-form articles using a single prompt. However, this has proven to be quite challenging. GPT-3.5 is a regressive model, meaning it does not consider the desired length of the output. If you prompt it to write a 500-word paragraph, it may deem the topic exhausted at the 300-word mark and stop there. This requires finding a workaround to trick the model into generating longer copies. In this article, we will share a method that enables you to generate a long-form article using a sequence prompt, and we will explore its potential using GPT-3.5.

The Problem and the New Solution

There are two main obstacles to creating a long-form article in one go with GPT-3.5: the regressive nature of the model and the memory limitation. In the past, attempting to generate a long-form article using a single prompt resulted in the model not following the initial set of questions provided. Additionally, around Step 6 or 7, the model would often start hallucinating topics unrelated to the initial prompt. However, with the introduction of the new 16k memory extension, these limitations have been overcome, allowing for a more coherent and focused output.

The Experiment: Generating a 10-Question Article

To demonstrate the efficacy of the new method, we will run an experiment using the GPT-3.5 model. The prompt consists of two parts: generating 10 popular questions about crypto mining at home and writing a 1000-word article using markdown formatting for each question. The prompt explicitly mentions the use of bullet points, lists, and tables.

The step-by-step process is as follows:

Step 1: Generating 10 Popular Questions

  • The first step involves generating a list of 10 popular questions about crypto mining at home. Each question serves as a subheading for the article.

Step 2: Writing the First Article

  • In this step, we take the first question from the list generated in Step 1 and write a 1000-word article using markdown formatting. This article will cover the topic comprehensively and include bullet points, lists, and tables where applicable.

Step 3-10: Continuing with the Remaining Questions

  • Following Step 2, we repeat the process for the remaining questions on the list, creating a 1000-word article for each question.

Step 11: Writing the Final Article

  • In the last step, we take the 10th question from the original list and write a 1000-word article using markdown formatting. This serves as the concluding piece of the long-form article.

Results and Observations

Upon executing the prompt and following the steps outlined, we observed the following:

  • The initial list of questions generated by GPT-3.5 successfully served as subheadings for the articles.
  • Each question was addressed in the resulting articles, with the exception of a glitch that led to one question being written out of order.
  • The generated articles were in markdown formatting and incorporated bullet points, lists, and tables as specified in the prompt.
  • The total word count for all articles combined came to a staggering 7700 words.


The experiment successfully demonstrated the effectiveness of the new method for generating long-form articles using a single prompt with GPT-3.5. By utilizing the sequence prompt and taking advantage of the new 16k memory extension, content creators can now create comprehensive articles covering multiple subtopics. Although some editing may be required to eliminate repetitive conclusions and intros, the resulting article is a substantial piece of content.

Moving forward, it's worth exploring the possibilities of using this method with other AI models and plugins such as Web Pilots and Box Script. The application of this method opens up exciting opportunities for content creation and enhances productivity for writers and creators. It will be intriguing to witness the further development and refinement of this process in the future.

We hope this article has been informative and entertaining. If you found value in this content, please consider liking, sharing, and subscribing. Stay tuned for more exciting experiments and insights.

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