The author concludes by sharing the results of running the workflow with a specific keyword, noting that while the final article wasn't perfect, it did automate a significant portion of the work. The author also shares some observations and learnings from the process, such as the need for more specific direction in prompts and the risk of plagiarism when using only one article as context. The article ends by suggesting future improvements to the agent, such as adding more evaluators and external tools.
Key takeaways:
- Large Language Models (LLMs) can be trained to assist in content creation, but they require human assistance to ensure the content is original and useful.
- Adding context to prompts can improve the quality of the generated output, and this can be done dynamically through a vector database search.
- Creating an SEO-driven content writing agent involves developing different roles and actions, such as an SEO Analyst, Researcher, Writer, and Editor.
- While the final article generated by the agent may not be ready for direct posting on a website, it can automate a significant part of the SEO research and content generation process, making it easier to create a good first draft.