Fueling Generative Content with Keyword Research

A blog post outlines the creation of a Python application designed to generate content ideas based on keyword research using two Cohere endpoints, Embed and Chat. The process involves three main steps: obtaining high-performing keywords using tools like Google Keyword Planner, grouping these keywords into topics through text embedding and clustering with scikit-learn's KMeans, and generating topic names via the Chat endpoint. High-traffic keywords are filtered and used to prompt the generation of blog post ideas, with the application outputting titles and abstracts for proposed content. By combining the capabilities of Cohere's Embed for text understanding and Chat for text generation, this approach provides a scalable means of producing content ideas that align with user demand and current trends.

Use this post, company, and trend context to find content marketing opportunities, perform competitive analysis, or address product feature gaps via the Plushcap MCP server or the Plushcap API.