AI content creation

Let's take a look together in of the future or rather the present day of content marketing.

According to Europol It will be in 2026 until 90th % of online content generated by AI. They monitor it for disinfo and deepfakes. In marketing communication, we deal with this for the visibility of the brand.

What percentage of content will you generate with AI in 9 months from now? The normal response is, “Nothing! Zero! Nada!” Especially since in the organization We don't have habits, mindset and purview. Today we will focus on those competencies.

How do you manage to automate or at least simplify the creation of marketing content?

You need to start distinguishing three concepts that are closely tied to your future marketing competencies.

1️⃣ Prompt engineering

2️⃣ RAG

3️⃣ Fine-tuning

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Prompt engineering

How do you enter commands into AI today? Sort of intuitively! Forget about intuitive command typing. This makes sense when you want quick advice from the model. It certainly has nothing to do with marketing communications in 2026.

Prompt-engineering is a set of techniques for entering commands into generative AI so that you get What are the best outputs. Repeatedly. Structurally. Systematically. No more magic.

Some techniques are useful when analyzing data. Others when you generate content as an individual. And a lot of them interest you especially when you are trying to automate content creation and interact with the model using the API. Because is not a command like a command.

And at the same time, prompt-engineering has limits that you can't overcome with a better command.

Prompt-engineering does not prevent hallucinations. It does not guarantee the error-free outputs. It doesn't address the facts. Solves statistics. When you are good at prompt-engineering, your output is much better than before! And at the same time, you can move on.

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Retrieval Augmented Generation (RAG)

RAG is a way to get your data into AI and reduce the risk of hallucinations. A large language model is loaded from an external vector database. While prompt-engineering lifts the quality of your outputs... RAG will ensure that they are based on your facts. Your brand, your business, your product information. If you want to create factually correct content, RAG is your best friend. At least currently

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Fine-tuning

General models are fine... and how about we get them fine-tuned for their needs? Like the model always writing texts like our brand? Or did he draw pictures in our visual? Or did he analyze the data in a specific way? Fine-tuning is used for this.

You limit the behavior of the model in some particular direction. Maybe he can write a word at the end of every sentence houbeles. Or he can write newsletters just like Radim Paris. It is enough to give him enough samples from which to start. Dozens are enough.

Prompt-engineering, RAG and FINE-tuning. What happens if you have these three areas in your pinky? You can automate the creation of marketing content for your brand through automation platforms. You have creativity. You have a tone. You have the facts.

14 days ago I would have told you that RAG and FINE-tuning on AI Marketing Lab they certainly won't and we'll stick with prompt-engineering. They are the purview of AI developers rather than marketers. Today, I'm pretty sure we'll include something from RAG & F-T.

The new version of the OpenAI API has been out for almost a week after all.

“There is no truth in business, only knowledge. “
- W. Edwards Deming

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