Have you ever tried using AI for tasks that require creativity, initiative, or vision? When formulating such a task, you don’t have a clear understanding of what you really want to see. But once you see it, you instantly know — this is it, or it’s not.
If you’ve ever tried, you know: you can ask an AI to write a text for you—you’ll get something no one wants to read. You can ask it to slightly refine your text — you’ll get something no one wants to read. Weird, huh? It almost never satisfies your sense of what’s okay.
At the same time, AI is getting better and better at working with codebase and logic operations, and it handles such requests quite well. In these tasks, AI is effective due to its ability to follow structured rules.
Every programming language has syntax rules, every popular framework has extensive documentation, and there are widely accepted best practices for naming files, functions, classes, and so on. Combining these provides clear boundaries of what we expect from AI as response (predicted output). Moreover, the presence of clear criteria for what constitutes a predicted output makes it easy to train models through backpropagation in such fields, bringing their performance to the top level.
Definition of creativity
Things are entirely different when it comes to creative tasks with AI. Creative tasks don’t have structured rules, documentation, or widely accepted best practices. There’s no concept of normality or a right answer for artworks. Each time, both form and essence are entirely a reflection of the creator’s worldview. And the form is no less important and unique than the essence.
Do you see any patterns or best practices in these paintings?
In creativity, the author’s inner worldview is reflected. This worldview is a perception, created and shaped by a cocktail of numerous events that have occurred in their life. A work of art is an imprint of this perception at the moment of the time when the work was created.
Anil R. Doshi, Assistant Professor at UCL School of Management, and Oliver Hauser, an economist at the University of Exeter, made excellent research on this:
Generative AI enhances individual creativity but reduces the collective diversity of novel content
Here are some quotes:
Having access to generative AI effectively equalizes the evaluations of stories, removing any disadvantage or advantage based on the writers’ inherent creativity.
If the publishing (and self-publishing) industry were to embrace more generative AI-inspired stories, our findings suggest that the produced stories would become less unique in aggregate and more similar to each other.
In other words, the chatbot made low-creative people more creative, but it made the whole group that had AI help less creative because it doesn’t have any diversity.
Okay, now we understand where the problem lies. The lack of a unique perception filter and criteria of predicted output makes AI practically useless for creative tasks. This is true, of course, if your goal is to create a result above the average.
As a result, we see that most people use AI solely for spell-checking, translation, or minor editing.
But can AI be integrated into creativity in a way that produces not mediocre results, but something truly worthy and unique?
Conclusion
It’s our personal and unique worldview perception and inner reflection make a text interesting to read and convert painting into true artwork. This imprint, which we leave on everything we touch, creates true value for us and other people.
Now we have open and accessible large LLM models as a foundation. Until recently, the costs of creating a personalized LLM model was too high for practical implementation of the described idea. Now, those costs are no longer prohibitive. And we have the opportunity to leverage all the capabilities of LLMs without losing the personality and diversity essential to the creativity field.