Using AI to Create Content for WordPress

One issue with using AI to create content for WordPress sites is that AI generated content doesn’t necessarily match your own or your brand’s writing style. In this Case Study I’ll show you an article generated by ChatGPT and point out some style issues. Then I’ll show you how I taught ChatGPT to match my own writing style better and finally, I’ll show you the resultant article.

Let’s start with a ChatGPT generated article. Here’s the prompt.

Write a 250 word article about using AI to generate WordPress articles for SEO purposes.

Here’s the output.

<Starts> Using AI to Generate WordPress Articles for SEO Optimization

The digital age has ushered in a new era of content creation. As businesses and bloggers strive to maintain an edge in the saturated world of online content, there’s a growing trend towards utilizing artificial intelligence (AI) to generate WordPress articles tailored for search engine optimization (SEO).

Traditional content creation can be time-consuming, and even with a team of skilled writers, the demand for fresh, relevant content can be overwhelming. Enter AI-driven tools. These platforms utilize advanced machine learning algorithms to draft articles that are not only coherent and engaging but also optimized for search engines. This means the content is tailored to rank higher on search engines, driving organic traffic to websites.

The advantages are numerous:

  1. Consistency: AI ensures a steady stream of content, crucial for websites that need regular updates.
  2. Optimization: AI tools can be programmed to follow the latest SEO best practices, ensuring articles are primed for high search engine rankings.
  3. Scalability: Instead of hiring more writers for increasing content demands, businesses can scale up with AI.

However, while AI offers numerous benefits, it’s essential to strike a balance. Over-reliance on AI-generated content can make a brand’s voice feel impersonal. Furthermore, readers value genuine human connection and insights, which AI can’t replicate completely.

In conclusion, integrating AI with WordPress for SEO-optimized articles offers an exciting frontier for content creators. It’s a tool that, when used judiciously, can propel online presence, but the human touch remains irreplaceable in crafting truly compelling stories. <Ends>

My comments

I don’t like this style much. There are phrases I wouldn’t use. For example, “the demand for fresh, relevant content can be overwhelming”.

These two sentences say pretty much the same thing.

“These platforms utilize advanced machine learning algorithms to draft articles that are not only coherent and engaging but also optimized for search engines. This means the content is tailored to rank higher on search engines, driving organic traffic to websites.”

To say the advantages are numerous needs to be followed up by more than 3 advantages in a list too!

But overall the writing is what I call low-bandwidth. It doesn’t actually say much and so delivers very little value to reader. I like my own writing to be concise and to convey useful information and to get ChatGPT to match my preference more closely I’ll need a better prompt or to upload a good sample of my own writing first.

LLMs in Qualitative Market Research

The intersection of voice-to-text technology and Large Language Models (LLMs), like, is creating a seismic shift in the landscape of qualitative market research. Traditionally, qualitative research has been a labour-intensive task, fraught with the challenges of capturing, transcribing, and analyzing vast quantities of unstructured interview data. However, as businesses strive to understand the nuances of consumer behaviour and preferences, the integration of advanced voice-to-text systems and LLMs is set to revolutionize the field, unlocking efficiencies and insights that were previously unattainable. Indeed LLMs in qualitative market research could potentially drive up productivity in a sector where interview analysis is an expensive process.

Voice-to-Text Technology: Capturing the Nuances of Human Speech

The proliferation of voice-to-text technology has been a game-changer in how data from research panels is collected. With the ability to accurately transcribe human speech in real-time and identify and keep track of individual speakers, this technology ensures that every opinion, suggestion, and subtle variation in tone is captured with precision. This not only streamlines the process of data collection but also preserves the richness and authenticity of the respondents’ voices. When applied to focus groups, interviews, and other qualitative methodologies, voice-to-text systems enable researchers to gather verbal data with unprecedented ease and accuracy. Furthermore, AI-driven sentiment analysis can identify positive and negative emotions along the way

Large Language Models: From Data to Decisions

Large Language Models, such as, represent the cutting edge of artificial intelligence in text analysis. These models have the capability to understand context, infer meaning, and uncover patterns within large sets of text data. By analyzing the transcriptions produced by voice-to-text systems, LLMs can quickly sift through the colloquialisms and intricacies of spoken language, transforming qualitative feedback into actionable insights.

The Synergy in Market Research

The synergy between voice-to-text systems and LLMs like is particularly transformative for market research. This combination allows researchers to:

  1. Increase Efficiency: Automation of transcription and preliminary analysis cuts down on time and resources spent on data processing.
  2. Enhance Accuracy: The integration reduces human error in data transcription and ensures that the subtleties of human communication are not lost.
  3. Scale Up: Researchers can handle larger volumes of qualitative data, making it possible to conduct more extensive and robust studies.
  4. Gain Deeper Insights: With advanced analytics, LLMs can identify trends, sentiments, and themes that might elude even the most experienced human analysts.
  5. Drive Innovation: By quickly identifying consumer needs and gaps, companies can pivot and innovate with greater agility.

Case Studies and Applications

Businesses across various sectors are leveraging this technology to stay ahead of the curve. For instance, a consumer goods company might use voice-to-text and LLMs to analyze customer feedback from social media, call centers, and focus groups to guide product development. Meanwhile, a healthcare provider might utilize the technologies to interpret patient discussions and improve care services.


The integration of voice-to-text systems with Large Language Models like is more than a mere enhancement to qualitative market research; it is a revolutionary step forward. LLMs in qualitative market research enable the efficient and accurate analysis of spoken data and this synergistic technology offers a deeper understanding of consumer behavior and provides a competitive edge to those who adopt it. As we continue to refine and develop these tools, the potential for new insights and innovations in market research is boundless, promising a future where businesses are more closely aligned with the needs and desires of their customers than ever before.