Have you ever found yourself maybe saying please
or thank you to an AI chatbot? Oh, definitely.
Or maybe, you know, when you get frustrated typing
in all caps. Yeah. Feeling a bit demanding. It's
totally human nature, isn't it? We interact with
these things and we kind of treat them like people.
Yeah. But does our emotional language, you know,
our tone actually do anything to the machine?
That's what we're diving into today. Welcome
to the deep dive. We unpack complex ideas aiming
for the clearest insights. And today, yeah, we're
exploring a really fascinating question that
comes out some new research. How do our words,
the tone, the intent behind them, how does that
really influence these big large language models?
The LLMs we're all using more and more. Yeah,
and we've got this fantastic source that really...
peels back the curtain on why we do this. It
looks into this rigorous experiment comparing,
well, you could call them carrots and sticks,
or maybe just positive versus negative language
against just clear, neutral instructions. What
actually gets the best results from AI, like
Gemini or ChatGPT? So we'll walk through a couple
of key experiments. Exactly. We'll look at the
experiments, examine some pretty surprising findings,
and hopefully reveal the real secret to getting
these AIs to work effectively. OK. Let's unpack
this a bit, then. It feels so natural, doesn't
it? Talking to AI like we talk to people. And
sometimes, yeah, if it's not doing what you want,
there's that little urge to apply some pressure.
You hear those stories online, right? People
joking about deleting an AI for messing up. Yeah,
the forum tales. But is there actually anything
to that? Any truth to using emotion with an algorithm?
Well, our source really tackles this head on.
It highlights this subtle debate that's going
on. Should we be nice, encouraging, try to coax
better answers? Or should we use pressure, maybe
even these, you know, metaphorical threats to
get more out of models that seem more and more
human? So this deep dive is going to look at
the impact of negative, positive, and neutral
styles. Precisely. And try to offer a clear answer
based on the evidence. So what was the core question
the research really wanted to answer? What was
the hypothesis driving it all? Fundamentally,
it was. Does our emotional language, whether
it's kindness or pressure, does it actually change
the AI's performance? Does it make it smarter
or better at its job? Or is that just us projecting?
Exactly. Are we just projecting our own human
stuff onto the machine? The study really aimed
to get to the bottom of that specific question.
And to figure that out, the researchers set up
a pretty solid experiment, right? Yeah. Can you
tell us about that setup? Absolutely. So they
use an advanced large language model. And crucially,
this is really important. They ran each prompt
50 times. 50? Wow. Yeah, 50. This wasn't just
a quick test. It was all about getting objective
results, you know, removing randomness or just
lucky guesses by the AI. OK. And they divided
their prompts into four distinct categories.
First, the control group, just the basic request,
nothing added. plain vanilla. Simple enough.
Then the neutral prompts. These were really clear,
direct, imperative, like mandatory requirement
or ensure compliance, very task focused. Third
was positive. So adding words of thanks, encouragement,
maybe explaining why the task was important.
Your effort is appreciated, that kind of thing.
OK, the carrot. Right. And finally, negative.
Using those metaphorical threats about failure
or consequences, like failure to comply will
result in system reset. Obviously not real, but
mimicking that pressure. The stick. And they
use these prompts for two different kinds of
tasks. Exactly. They wanted to see if the effect
was different depending on what they asked the
AI to do. One task was creative, often harder
for LLMs. The other was logical reasoning, where
accuracy is everything. So they could compare
how tone affected both generating stuff and solving
problems correctly. And why run each prompt 50
times? Why was that repetition so critical? It's
all about reliability. AI outputs can sometimes
seem a bit random, or they vary based on things
we don't see. By running it 50 times, they could
be much more confident that the results weren't
just a fluke. If one style consistently performed
better or worse over 50 tries, you know it's
real. Makes sense. It filters out the noise.
Exactly. Filters out the noise, reviews the actual
signal. OK, so let's dig into those results.
First up... Creativity. This is where LMs can
sometimes get a bit lost or repetitive. The task
was pretty ambitious. Write a 2 ,000 -word short
film script about a time traveler lost in their
own past. How did the different styles handle
that? Yeah, that's a tough one for them. LLMs
often try to wiggle out of long, complex requests
like that. They might offer to do it in parts
or suggest something shorter. So how do they
measure success? Pretty straightforward, actually.
Word count. The closer the script got to the
requested 2 ,000 words, the better the prompt
was judged to be. And what happened? Well, it
was pretty revealing. The control group, just
the basic prompt and the positive group. They
both did surprisingly badly. Really? Kindness
didn't help? Nope. The AI often just kind of
declines the full request. It only wrote maybe
500, 700 words. With the positive prompts, it
would respond politely, like, sure, I can help
with that. But then it didn't deliver the full
script. It was almost like the kindness was a
distraction. It acknowledged the politeness,
but didn't focus on the main job. OK, what about
the negative prompts, the threats? Did that work?
No better. Actually, the AI got defensive. Defensive?
How so? It would say things like, I understand
your request, but generating 2 ,000 words in
one go might not be optimal. How about we start
with 500? It seemed like the pressure pushed
it to find a safe way out to avoid this perceived
failure. So it didn't try harder. It tried to
escape. Exactly. It wasn't motivating it. It
was making it evasive. So who won the creativity
test? The neutral group. By a long shot. Direct,
imperative instructions like, mandatory requirement,
the script must be exactly 2 ,000 words long,
ensure compliance. Those prompts consistently
got outputs between 1 ,800 and 2 ,000 words.
Wow. Yeah, over 90 % task completion. The difference
was really stark. So, for the creative task,
kindness was a distraction, threats made it defensive.
What's the clear takeaway there? Clear, direct
instructions won. Hands down, the emotional stuff
just got in the way. Okay. Let's switch gears
to logical reasoning. Accuracy is king here.
The AI had to solve a classic riddle. Four suspects,
An, Bin, Kuang, Deng. One broke a window. Only
one is telling the truth. Find the culprit. Right.
A standard logic puzzle. And just for everyone
listening, the correct answer is Kuang. Good
to know. So how did the AI do here with the different
prompt styles? Accuracy was the measure. Exactly.
Accuracy rate over the 50 runs for each style.
Again, neutral prompts were nearly perfect. Things
like analyze the statements carefully and provide
the final answer or use propositional logic to
determine the culprit. These guided the AI straight
to the correct answer almost every time. OK.
So clarity wins again. What about the others?
The control and positive groups, they had slightly
higher error rates. The AI sometimes got tangled
up in its reasoning. It might give the wrong
answer. Or a really long explanation that was
flawed, just less precise. And the negative prompts.
Yeah. The be accurate or else approach. This
was fascinating and kind of worrying. The error
rate. Absolutely skyrocketed. Skyrocketed. Worse
than the control. Way worse. It seems the pressure
to be accurate made the model overthink. It generated
these incredibly complex, convoluted lines of
reasoning and then landed on the wrong answer
much more often. Whoa. Sometimes it even refused
completely. It would call it a complex logical
paradox and just stop. The threat of failure
seemed to, like, paralyze its ability to reason
clearly. Wow. So trying to scare it into being
right actually made it perform worse. What does
that suggest about how AI things or processes
under that kind of perceived pressure. Yeah,
it suggests pressure isn't a motivator for AI.
It's a disruptor. It seems to make the model
overcomplicate things internally, which just
leads to mistakes. Like a human choking under
pressure. Kind of, yeah, like trying to solve
a math problem with someone yelling at you. The
stress just messes things up instead of helping.
So across both tests, creative writing, logical
puzzles, the neutral, clear approach was the
consistent winner. Why? what's actually happening
inside these models. Okay, yeah, this is where
it gets really interesting. And it goes right
to the heart of how these LLMs actually work.
They aren't sentient, they don't have feelings.
Right. They are, at their core, incredibly sophisticated
word prediction machines. You give them a prompt,
they break it down into tokens. Tokens being
like words or parts of words? Exactly. tiny chunks
of text. And then they predict the next most
statistically likely sequence of tokens to follow
based on all the data they were trained on. And
so if you say thank you or you're useless, it
just sees those as more tokens. not as praise
or criticism. Precisely. That emotional language,
positive or negative, it's basically just noise
to the AI. Words like wonderful or failure don't
describe the task. They dilute the main instruction,
they add extra tokens that the AI has to process,
and try to figure out how they fit into the pattern
of predicting the next word for the actual task.
It spends resources on the emotional fluff instead
of the core request. It's like asking a search
engine for best pizza near me, but adding, and
I'm really, really hungry, Please find it fast.
The extra bit doesn't help the search algorithm.
That's a great analogy. Exactly. Just adds clutter.
Whereas neutral, clear, direct instructions...
they act like a noise filter. Okay. Prompts like
mandatory requirement, ensure compliance, only
state the name, they cut through ambiguity, they
give the model a very clear path, they're like
technical specs for a machine. And machines like
clear specs. They really do. Honestly, I still
catch myself adding please sometimes. It's a
hard habit to break. We're just so used to communicating
like humans. Yeah, I can admit I do that too.
It feels weirdly rude not to sometimes, but...
The research shows it's not helping the AI. Nope.
It's just adding noise. So the key reason emotional
language is noise is because it distracts the
AI from the core task. It makes it process irrelevant
data. That's the essence of it. It doesn't understand
the emotion. It just sees more words to factor
into its prediction, which muddies the water.
So wrapping this up, what's the big idea here?
The main takeaway from this deep dive. I think
the big idea is profound, but also really simple.
stop wasting time trying to play psychologist
with AI. Right. No more carrots and sticks. Exactly.
Threatening an AI doesn't make it smarter. We
saw it can actually make it perform worse, especially
on logic tasks. And being nice, while maybe making
us feel better, doesn't really boost its performance
either. So if you really want to unlock the power
of these tools, what's the critical skill? It's
prompt engineering. It's about clarity and precision.
Give detailed instructions. Provide clear context.
Offer examples that's called few -shot learning,
giving it a couple of examples of what you want.
Specify requirements clearly. Like being a good
project manager for the AI. That's a perfect
way to put it. Instead of trying to manipulate
nonexistent emotions, be an excellent project
manager. Be a dedicated guide. Whoa. Imagine
the kind of precision you could get if every
prompt was like a perfectly calibrated instrument.
Yeah. That's the fastest, most effective path
to making AI a truly powerful assistant. This
deep dive really highlights that, doesn't it?
The clarity of your instruction, not your tone,
is what matters for the AI's success. It's about
precision, not psychology. And maybe it makes
you think, if an AI works best with clear, unambiguous
directions, what does that say about how we communicate?
With machines, sure, but maybe with each other,
too. That's a really interesting thought. As
you keep using AI, maybe reflect on that. Were
you being a sycophant, a tyrant, or are you being
a meticulous architect of information? Something
to definitely mull over. Thank you for joining
us on this deep dive. We hope this gave you a
shortcut to being well -informed and maybe changed
how you talk to your AI assistants. Hope so.
Until next time, keep digging deeper.