The Need for (Re) in Generative AI

Let’s start by exploring why the term (Re)Generative AI emerged and why there’s a need to distinguish it from “regular” Generative AI.

Generative AI, especially that based on Large Language Models (LLMs), simply creates text based on an input prompt. Using its vast training database, it constructs sentences by utilizing its weights and the probabilities of words that should appear in them.

At first glance, this seems like an excellent solution for creating marketing content on a massive scale. However, upon closer inspection of the content it produces, we quickly realize that it’s not always of adequate quality and can sometimes be entirely incorrect, inappropriate, or fabricated.

Hallucinations: The Achilles’ Heel of Generative AI

This brings us to one of the biggest problems with generative AI models: hallucinations. Currently, leading AI model companies are working hard to reduce hallucinations in their models and are investing enormous resources into this effort. To a large extent, they’re succeeding, but not always and not in every case. Much depends on the topic we give the AI model. The less general and more specific the subject is to a particular field, requiring more professional and specialized knowledge, the worse the model performs.

Why GenAI Struggles with Niche Expertise

This all stems from how generative AI is built. More specialized, niche topics closely related to a specific field naturally occupy a smaller portion of the AI models’ training base. As a result, the neural networks activated in the text generation process use and mix output text from the general knowledge base. Unfortunately, this leads to hallucinations – the creation of text with false, inappropriate, or illogical information and conclusions.

Overcoming the Limitations of GenAI in Marketing

This, of course, causes huge problems when creating marketing texts and content. When we want to promote our product or service, we want the text and content to be closely related to that product/service and our industry, which is also specifically defined. Unfortunately, a generative AI model, even with the best-adapted prompt, won’t always handle this well, and prompt engineering alone will only marginally improve our results.

For this reason, and due to these needs, a different approach to harnessing AI’s potential has emerged.

What is (Re)Generative AI?

(Re)Generative AI can be simply described as a super-customized generative AI for a specific context.

It’s an enhanced Generative AI with additional context, content, media, and other resources related to a company’s activities and its industry. ReGenerative AI uses existing company content as its main knowledge base and uses it to create very well-tailored marketing materials that can be successfully used in social media and company blogs.

(Re)Generative AI in Action: A Case Study

This is how our AI SEO Team works, which is a perfect example not only of how (Re)Generative AI operates but also of how it’s an effective SEO tool. AI SEO Team focuses primarily on ensuring that the content it creates is useful for customers, meets all Google guidelines, including the very important E-E-A-T, and is precise and relevant.

Beyond (Re)Generative AI: The Power of Orchestration

(Re)Generative AI, when properly implemented, is a very powerful tool. However, in our solution, we didn’t stop at just using (Re)Generative AI; we further strengthened it with additional workflows that use AI agents (hence the ‘team’ in the solution name) specialized in specific tasks.

These specialized AI agents work together to address specific tasks, enhancing the overall capability of (Re)Generative AI to deliver customized marketing solutions.

Ensuring Quality: Self-Checks and Feedback Loops

Particularly noteworthy here is Content Critique, which perfectly complements (Re)Generative AI. The content is not only basically adapted and prepared according to the company’s brand, tone, and industry, but it’s also evaluated and adjusted until it meets all very rigorous requirements.

Tailoring Content for Different Platforms

Content should look different when it’s to be used on a company blog, different on social media like Twitter, and different on Facebook or Instagram.

(Re)Generative AI allows for content to be crafted differently for various platforms, ensuring maximum engagement and effectiveness.

Final Thoughts

Summary (Re)Generative AI is a new concept in the industry created to fill the void in the space of solutions based on Generative AI but appropriately strengthened and adapted to eliminate the biggest drawbacks of Generative AI.

(Re)Generative AI is a powerful marketing tool that not only allows for engaging customers with content but is also enormously effective when it comes to SEO.

AI in marketing is undoubtedly a trend that will continue to develop. We see enormous potential in the application of AI and customer interest in this topic.

This is not surprising because, with the possibilities that AI, and especially (Re)Generative AI, gives us, any company that misses this trend will very quickly be left behind by those who are already using it.