Harnessing the full potential of generative AI to produce targeted use cases can be challenging. Generative models are so broad that it's difficult to define precise objectives. Here, we describe a quick mental exercise you can do to identify use cases for generative AI within your workflow.
The best approach is to integrate generative AI within contexts you already understand. An analogy that we use is: AI is something like what the tractor was for the farmer. Our current attitude towards AI is that it is more like a cyborg farmer which does everything a farmer can do but better. But this is not the case. In all my years of working with AI and helping people wield AI, I’ve never seen it make a person into something they were not before. Just as a tractor never made a person who didn’t know blip about farming into a farmer, AI will not imbue you with special skills. But if you ARE a farmer, then a tractor is an impact multiplier. And those who refuse to start using the tractor will inevitably fall behind.
So what is the next step? The next step is to ask yourself: what am I already doing today? So, for example, how can you take this blog that you're writing and instead of writing one publication at a time, you can write a thousand at a time, and surface that to people. So next time you can just automate the process and run a loop, making the content-generation process much easier and faster.
Tier 1: Hand off elements of your workflow involving content generation
Start small by automating specific generative tasks you routinely perform as part of your work. Think critically about how AI content generation could assist you in producing the same type of content faster and more efficiently. Experiment with different prompts and models to optimize the output for your specific use case.
Tier 2: Make the process scalable
Once you get comfortable with utilizing generative AI for specific tasks, consider how you can automate the process, to crank out that content by the thousands. This is where the art of creating stable prompts comes in. A stable prompt can output what you created reliably over and over. You will still need to monitor the output for inconsistencies, but that will be the sole task left from what used to be a taxing writing/image creation endeavor.
Tier 3: Make the process personalizable
The final step is adding an element of personalization that allows you or others to change elements of the prompt and still get reliable outputs. Figure out how to modify the prompt to produce variations that meet a more diverse set of goals than what you initially solved. This grants the ability to utilize your scalable workflow for a wider array of personalized purposes,
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or, in case of image generation
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