The delivery of production is inseparable from the entire concept of division of labor. Now the ability of LLM can give you a try to play a division of labor arrangement game, which will be very interesting.
The output of A can be used as the input of B, the output of B can be used as the input of C...and so on.
This is the most ideal division of labor, and data flow and judgment are very important. I believe it can be achieved with the cooperation of LLM, so let’s continue talking about what can be achieved?
If you want to make it effective, give up your personal fantasies, and look for opportunities in social scenes. What is needed for employee cooperation? You have to seize this to optimize the process.
Finally, you will find that the embedding of AI in work shortens and merges the current work scenes to a certain extent. It solves the problem of time and improves work efficiency from the merging of scenes.
The advantage of this is that you will make your AI more focused in terms of capabilities and attention, your fine-tuning, iterations, and database updates will be more directed, and time will have the necessary value in this process.
But on the contrary, you expect a prompt to be omniscient and omnipotent to solve a problem. Dialectically speaking, it also increases your verification and optimization costs, because you have to make necessary allocations within limited capabilities.
Therefore, try to break complex things into a chain. It does not need to be long, but the scene must be clear enough. Each link saves 5 minutes. 5 links can save 25 minutes. This is actually an amazing process. .
Of course, we can do better and save more time. By the way, don’t forget the issue of confirming responsibilities and authority. The content generated by AI also needs to be borne by people. AI will not have KPI assessment.