When it comes to technology like large language models and text–to–image models, we’re still in the early days. The majority of people haven’t yet adopted these tools for their work, hobbies, or day–to–day lives.
Some have experimented with early versions, such as GPT–3, and found them lacking, abandoning ship before GPT–4, with its significantly improved capabilities, came into wider use. Even those who have found some applications often don’t venture beyond their starting point, opting to use these models for basic outputs.
Just recently, I assisted a friend who runs a one–person business with employing ChatGPT Plus as a virtual marketing consultant. We used the tool generated content and strategic ideas for the company. Yet, like all things, GPT–4 has its limitations. It needed many guiding questions and iterative refinements to produce truly valuable output. If you just skim the surface, it will do so, too.
And while I believe the term “prompt engineering” is overhyped, I do think a particular mindset is necessary when working with neural models: you have to guide and steer them. It’s an iterative refinement process, and anything less will yield rather superficial results—at least as things stand now.
So, if you’re anxiously searching for an avenue to break into the AI field, concerned you’re already behind due to the progress of others, here’s a tip: learn to prompt well and then help somebody who can’t.
Here you can find a related, brief, yet fairly typical tale from a Reddit user who took a data analyst position in a company. They’ve been using ChatGPT to increase their productivity, but feel a bit shy about admitting this to their superiors. This story resonates with me. While large corporations may face difficulties in implementing AI from the top down, employees are able to adapt and use it from the bottom up quite readily. However, without any direct financial motivation, they might not necessarily share these productivity gains with the rest of the organization.
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