To ensure that your content-generation tool creates coherent long-form pieces, here are five ways you may implement:
Chunking and Overlap: When creating text, divide the content into smaller portions or chunks. This helps to keep context between paragraphs. For example, when creating a new paragraph, add the previous paragraph's last few phrases to provide context for the model.
Consistent Prompts: Use prompts that help the model stay on subject and maintain the tone or style of the article. Starting each part with a clear direction can be beneficial.
Fine-tuning using Coherent Datasets: Test your model on a dataset of high-quality, coherent long-form texts. This allows the model to learn patterns of coherence and flow in larger texts.
Human-in-the-Loop Review: Create a review process that allows a human editor to provide feedback on the created content. This can assist in identifying fragmented areas early on and direct the model to more coherent outputs.
Post-Generation Editing: After producing the entire text, use a coherence-checking algorithm or a second model to assess the flow of ideas and recommend adjustments. This helps to ensure that the finished product reads properly as a coherent unit.
Following these steps, you can maintain coherence in long text generated using GPT or, in this case, a chatbot based on the GPT model.
Related Post: How to handle context window limitations when generating long text with GPT models