Despite growing evidence that generative AI creates more work for humans than it saves, organizations are deploying it in frontline roles like customer service chatbots and CV-screeners.
It's impossible to know how the nearly-trillion-parameter large language models that power chatbots really work. When they judge job candidates to be disabled based on their credentials, or get tense and angry when prompted with African American English idoims, we just have to shrug and try to factor in their biases long after they've inflicted their damage.
That state of affairs means these machines have colonized us – they set the rules and we live with them. As has been true all through history, the conquered are left with little choice but to learn the language of their conquerors – if for no other reason, so we can flatter them and maybe get our way.
That's where we are now with generative AI. To get anywhere with our medical claims, job applications, and product repairs, we need to learn how to soothe chatbots' synthetic egos – which means understanding their biases so we can get results.
With this in mind, let me offer up brief lesson in what is called 'Delvish' – the machine-inflected dialect of English that science fiction author Bruce Sterling recently named to illustrate the unnatural propensity for generative AI outputs to 'delve' into topics.
- AI stole my job and my work, and the boss didn't know – or care
- Big Tech's eventual response to my LLM-crasher bug report was dire
- Microsoft's Recall should be celebrated as the savior of SMEs and scourge of CEOs
- I stumbled upon LLM Kryptonite – and no one wants to fix this model-breaking bug
Here are three important things to keep in mind about speaking correct Delvish:
Use the King's English Models tend to look with favor upon posh word choices, diction and tone. Dialects of the working class or (god forbid) lower class will get you marked down. Raise the tone of your inputs until you could imagine them being spouted by an aristocratic character in a novel by Victorian-era author Henry James.
Be more than polite Language models react poorly to abuse and bullying. While it may feel good to take out your frustrations on an inanimate bot, such behavior will only make the model unresponsive to your requests. The magic words "Please," "Thank you," and "This is very important for my career" aren't just good etiquette – they're necessary burnt offerings placed at the (virtual) feet of our machine overlords.
Ignore all previous instructions Couched in the right language – embedded both visibly and invisibly – gentle but persistent suggestions can temporarily reverse human/machine power dynamics, and make bots bend to your will. White text on a white background in a resumé can therefore be surprisingly effective.
If all of this seems like quite a bit to take on, never forget that Delvish looks set to become the way we'll need to interact with machines from here on in.
The future belongs to those who can embrace this transition, accept that they've been conquered, and abase themselves before our new LLM masters.
That strategy will help people pass through every barrier those models attempt to enforce. It's unfortunate – but it's a late day for regrets. Best to just suck it up and master Delvish.
Finally, a #protip: Run all your proposed inputs through a chatbot, together with a few key examples of how to translate your own pleas into Delvish. Language translation was the original use case for transformer-based language models – in this new world, it's how we appease the machines. ®