n8n workflow template · 18 nodes · OpenAI
Automate Blog Creation in Brand Voice with AI is a free n8n workflow template that starts when you run it manually in the n8n editor and works with OpenAI and WordPress. It has 18 nodes, 6 of them AI nodes (OpenAI Chat Model, Information Extractor and Basic LLM Chain). The notes inside the workflow say: “First, we'll need to gather existing content for the brand voice we want to replicate.” To run it, you add your own credentials for OpenAI API and WordPress API.
The text below comes from the sticky notes inside the workflow. We removed links, code samples and anything that looks like a key or token.
Read more about the HTML node
First, we'll need to gather existing content for the brand voice we want to replicate. This content can be blogs, social media posts or internal documents - the idea is to use this content to "train" our AI to produce content from the provided examples. One call out is that the quality and consistency of the content is important to get the desired results.
In this demonstration, we'll grab the latest blog posts off a corporate blog to use as an example. Since, the blog articles are likely consistent because of the source and narrower focus of the medium, it'll serve well to showcase this workflow.
A. Yes! This approach can use other source materials such as PDFs, as long as they can be produces in a text format to give to the LLM.
Learn more about the Markdown node
Markdown is a great way to optimise the article data we're sending to the LLM because it reduces the amount of tokens required but keeps all relevant writing structure information.
Also useful to get Markdown output as a response because typically it's the format authors will write in.
Read more about the Basic LLM Chain node
Our approach is to first perform a high-level analysis of all available articles in order to replicate their content layout and writing styles. This will act as a guideline to help the AI to structure our future articles.
Read more about the Information Extractor node
Second, we'll use AI to analysis the brand voice characteristics of the previous articles. This picks out the tone, style and choice of language used and identifies them into categories. These categories will be used as guidelines for the AI to keep the future article consistent in tone and voice.
Read more about the Information Extractor node
Finally with this approach, we can feed both content and voice guidelines into our final LLM - our content generation agent - to produce any number of on-brand articles, social media posts etc.
When it comes to assessing the output, note the AI does a pretty good job at simulating format and reusing common phrases and wording for the target article. However, this could become repetitive very quickly! Whilst AI can help speed up the process, a human touch may still be required to add a some variety.
A. No! I would recommend storing the results of the AI's analysis and re-use for a list of planned articles rather than generate anew every time.
Learn more about the Wordpress node
To close out the template, we'll simple save our generated article as a draft which could allow human team members to review and validate the article before publishing.
The text below comes from the sticky notes inside the workflow. We removed links, code samples and anything that looks like a key or token.
With such an approach, it's possible to generate a steady stream of blog article drafts quickly with high consistency with your brand and existing content.
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The 18 nodes in file order (sticky notes excluded). Branches and connections determine execution order in n8n:
Configure your own credentials after importing. Create each credential in n8n, then select it in the node.
Do you need somewhere to run it? Read our guide to self-hosted n8n.
We can adapt this workflow to your apps, data and process, then deploy and support it.
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