Just about every leadership team out there is pressuring their employees to use AI to do more with less. Unfortunately, they:
And IT either prohibits connecting AI to your core systems or provides very little guidance on how to do so safely.
It's kind of a mess out there, and AI hype is at the center of the storm.
Which is why we're excited for this guide. It explains why AI hallucinates, what we can do about it, and which use cases make the most sense given AI's limitations as of today.
Have you worked hard on your prompt library and gotten solid outputs only to check again a week later and notice every ouput is...well...bullshit?
There's a reason for that and this guide explains it.
LLM neural networks don't operate like animal neural networks do, and sometimes the connects it makes aren't based on rationale any living being would follow. Which is why it's reasoning for why it reached a conclusion can't be trusted.
Have you ever wished there was some kind of decision tree to help you figure out whether trying AI in a use case is worth the effort?
Now you have one.
It's a combination of organization risk tolerance and statistical inference based on the current benchmarks related to your project.
It helps determine which systems should be AI-hands-off and which are worth doing so you can divert human resource spend on something more productive.
When you boil it all down, AI is only worth using when you can measure the impact of the change.
Did your efficiency go up or down?
What impact did it have on your internal and external stakeholders?
In other words, you have to continuously evaluate and prove that your AI layer is worth keeping. And trust us, it's an exercise worth doing because upper management is going to ask you to perform this task more than once.
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