"Deliberate Creation": Creativity, Productivity, and the Necessity of Using the Right Tools
Plus a Plug (at the Beginning and End) for a Professional Productivity Bootcamp
The Plug
As this is a free blog, and as I have a consulting business that I run, I assume that you will forgive me for advertising briefly for a commercial function.
In coordination with Steve Hargadon of Learning Revolution, I am hosting a bootcamp on professional productivity with ChatGPT and other AI tools! You can register without feeling like you need to attend all three sessions, because recordings will be available forever afterward.
$149/person, $599-999/institutional license
For more information scan the QR code below, or go to this link:
https://www.learningrevolution.com/professional-productivity
Creativity and Productivity: Two Iterations of Creation
Although there are many different skills and abilities that we can enhance using generative AI tools, the two most broad, and the ones on which I think we should be mostly focusing, are “creativity” and “productivity.”
Mostly, when we think of creativity and productivity (at least when we think of them in our roles as employees or administrators), we think of them as polar opposites. If you are spending your time being creative, then you are not being productive toward your work goals or projects. On the other hand, productivity in the workplace can be seen as fostering a lack of individuality and a stifling of the creative urges that we all have.
Fortunately, in the last decade, there have been multiple institutions and individuals speaking and writing about the connection between individual creativity and workplace productivity. Not only can creativity enhance work products, but the process of creation can often improve the rate of productivity of workers (Boyles, 2022).
Ironically, it was only when I experienced the processes of creativity and productivity while working with an artificial intelligence that I learned something about my own creativity and productivity. In guiding the AI tools through this process, I learned that the process in my own head was remarkably similar for private creativity and workplace productivity.
At some level, I had always known this (why else would I insist on spending a significant amount of time designing the header styles for my procedure documents at work?). However, the metacognitive processes of directing the tools through each step (and redirecting when they made mistakes) shed new light on these processes and ideas.
Creativity and Productivity are both centered around the creation of something, for specific goals, and for a specific purpose. They are impacted by the role we are playing when we design certain products. All of these elements are necessary for good prompts. At the center of both of these processes is a sort of “mindful” or “deliberate” “creation.” The finished products for these are more formal when we are being “productive” and more personal and informal when we are being “creative.” A good workplace permits and encourages “creative productivity,” which blends both of these, but that is a conversation for another day (and an entirely different blog).
How Do We Prepare to Collaborate with AI in “Deliberate Creation?”
Now that we realize that productivity and creativity are both part of the same process, how can we incorporate AI into that process? Also, how can we make sure that the AI tool does not take over the process? If that were to happen, we would lose our voice. If it were to happen repeatedly, we could become AI-dependent. In a world in which our tools can seemingly communicate and have minds of their own, we need to be sure that they do not boast or magnify themselves against us, whom are their creators and users. Those are human attributes, and we should not be fooled into thinking that our AI tools are smarter than we are. They are not smart at all.
Fortunately, Dr. Jeanne Beatrix Law has created a way to guide our thinking about the “deliberate creation” process and the healthy integration of generative AI tools. With her colleagues at Kennesaw State University, she developed a “Rhetorical Shot Engineering” Framework. Initially, this was developed for “prompt engineering,” but eventually she realized that the framework should focus more on the objectives and features of the finished product rather than the prompt itself.
Before any prompting is done, the creator must consider the purpose audience, tone, genre, style, context, and desired content of the material. As an English teacher, she focused on the writing form of creation. However, this framework would probably work with almost any other format.
Dr. Law was not content with simply putting this model out there. She also built a Custom GPT around this framework. Her goal was to help train students in best practices regarding AI collaboration in educational contexts, but this GPT can do much more (as can any AI tool, if you know how to use it). I encourage you to use this framework as you ideate regarding possible products of AI tool conversations.
How Do We Put That Into Our Conversation with AI Tools?
The answer lies in another framework, one about which I have written before. When you prompt after using the Rhetorical Shot Framework, be sure to use the COSTAR Framework: Context, Objective, Style, tone, Audience, Response. Are you seeing similarities? Many of the aspects you consider through “deliberate creation” can be transferred into this framework. If you do you want to use the headings, you do not have to. These headings just help me think about the elements that are that most crucial to success with generative AI tools.
Using the Right Tools
This concept is one of the most important, and it is one of the most misunderstood by all types of users: you have to use the right tool to get the right product, and you have to use that tool according to best practices. As I currently live in Idaho, and was raised in Utah, I have some experience with agricultural tools. Suffice it to say, you do not use a hoe to thrash. Nor to do you use an axe like a saw. Both of these pairs are used for similar environments or purposes, but their function is different. While we may need to produce multiple formats, we cannot truly use the same tool for all of these purposes. This is why we have all manner of tools.
Right Tools: Format
This may seem like a no-brainer, but you should only use generative AI tools for the products, formats, and purposes for which they were intended. You should not try to use Transformers (text) as Diffusers (Image and Audio), or vice versa. For example, other than Ideogram there are no image diffusers that can accurately generate text in images (and Ideogram is slowly getting worse at this).
You should also not try to use a text generator to create specialized files (presentation slides or PDFs). Chances are, this will not work. You must think through the processes you need to fulfill and then select the most appropriate tool for your purposes or desired outputs.
Truly multimodal tools, such as GPT4o, have been created using a variety of tools meshed into one to form a group of tools that acts like one tool. For example, you can call a database search help tool to help you research, ask GPT4o au natural to help you summarize your findings, and then upload a file and have your summary formatted according to that file’s format. Then, you can call an image generator tool (Dall-E 3) in the same conversation and have it create an image. In this conversation, you are really using multiple tools but they are acting like one because you are engaging (from your point of view) in only one conversation.
These distinctions can be a lot to take in, but they are necessary if one wants to have the most effective “deliberate creation” experience. Knowing the nature of these tools can help you select which tool you want to use.
Right Tools: Generalized vs. Specialized
This brings us to the next aspect of choosing the right tool for your creation process: a general tool vs. a specialized one.
If anyone has heard me talk about the benefits of open access and open-source AI tools, they have heard me talk about the dangers over over-hyped commercialized AI “ChatGPT wrappers” (a term that I first heard from someone who was creating one of these tools). These commercial “specialized” tools take the APIs from foundation models (GPT, Claude, Llama, etc.) and try to make them produce products for which they were not intended. There are hundreds (if not thousands) of these types of tools on Futurepedia.io. I only linked to it because there are beneficial tools featured on there. However, these specialized tools have several problems:
As I stated in my post on AI Feasibility, many commercial AI tools are created to trap you in a subscription plan.
Many commercial specialized tools have “freemium” plans and severely limit the abilities of free users.
Specialized commercial tools often track and commercialize user data, which is an extreme hazard in terms of confidentiality
Finally, the “abilities” of the specialized tools are often the same as those of the foundation models, or are often only baby steps ahead of the abilities of generalized tools (e.g., offering to convert the output of a conversation which is on par with ChatGPT into a PDF format automatically, when you could take three seconds to copy and paste the output into a Word document and save it as a PDF yourself).
In most cases, you should only use generalized tools for interactions with genAI tools. ChatGPT, Claude, and Gemini or Bing (if you prefer) are all you really need. Just use their multimodal capabilities (with the caveats I stated above) and you should not have to pay for any specialized subscriptions. If you have a use case (creating a logo with text, or creating music) that cannot truly be fulfilled by a multimodal tool, then go look at my list of free and open AI tools according to format that I created for work.
If you need more convincing on this topic, both David Wiley and Ethan Mollick agree with me. In fact, David Wiley compares the trend of using specialized tools over generalized ones to the adoption of the “learning styles” paradigm in education.
To be brief, in the 1960s educators and others were convinced that every student primarily had “visual, kinesthetic, or auditory” learning styles. For decades (and even still today), educators were obsessed with “teaching all three learning styles.” According to Wikipedia, as many as 70 models for thinking and teaching according to learning styles were developed. In the 2010s, critical thinking regarding these theories determined that there is no such thing as learning styles. They do not exist. Yes, we may prefer to hear, read, or feel, but those are not linked to our abilities to learn. In a similar way, the “utility” and “necessity” of specialized tools is debunked when you critically examine
how they work, and
how easily you can do the same work without complete AI automation.
Conclusion
I recently (in fact, while I was writing this post) read a LinkedIn post that claimed that essentially everything being considered regarding AI tools was overhyped. The author stated that “there is no AI Revolution” and that both generalized and specialized AI tools would soon be retracted from public access. Creators and providers would focus on narrow use cases and local use.
While I understand where the author is coming from, I disagree with their ultimate conclusion about the AI Revolution. It is going to come, but it will not be anything like what we are expecting. It will not come with automation, but it will come with collaboration. It will not come with AI assistants that generate completely perfect outputs every time. It will come with future-ready, tech-savvy professionals who know how to use AI productively and creatively. It will come with “deliberate creators” who know how to collaborate with AI.
The Plug (again)
As this is a free blog, and as I have a consulting business that I run, I assume that you will forgive me for advertising briefly for a commercial function.
In coordination with
of Learning Revolution, I am hosting a bootcamp on professional productivity with ChatGPT and other AI tools! You can register without feeling like you need to attend all three sessions, because recordings will be available forever afterward.$149/person, $599-999/institutional license
For more information scan the QR code below, or go to this link:
https://www.learningrevolution.com/professional-productivity
References
Boyles, M. (2022). “The importance of creativity in business.” Harvard Business School. Jan 25, 2022. Retrieved on June 12, 2024 from https://online.hbs.edu/blog/post/importance-of-creativity-in-business.