Have you ever typed a prompt into one of those
big AI models? You know, asking for something
really new, maybe a bit out there. And what comes
back is just the safest, blandest, most corporate
thing imaginable. Yeah. Exactly. It feels like
talking to a wall sometimes. A very expensive
wall. It's that specific quiet frustration, isn't
it? Like this giant brain is just, I don't know,
afraid of its own thoughts. Welcome to the deep
dive. And the common idea is, well, the AI just
isn't smart enough for real creativity. But our
deep dive today, looking at some really interesting
work from Stanford, suggests something else.
Yeah, it seems the creativity is actually in
there. It's just kind of locked away behind this
really stubborn safety filter. That's our mission
for this deep dive. Right. We want to reveal
the simple non -technical key to that lock. We're
talking about basically one short instruction.
maybe 10 extra words or so. Yeah, roughly. And
it can transform tools like ChatGPT, Claw, Gemini,
make them feel more like actual creative partners.
OK, so let's unpack this. We're going to cover
three main things today. First, why your AI seems
stuck in boring mode, that whole safety issue.
Second, we'll introduce this technique. It's
called verbalized sampling. Explain how that
trick actually works. Got it. And finally, maybe
the best part, we'll give you some practical
copy paste prompts you can start using right
away. Yeah. get those better results. OK, so
first step, why is the AI so average? Well, the
main cause is pretty straightforward, really.
It's safety engineering. Right. Companies, you
know, they spend billions training these models
to be incredibly safe. They have to avoid anything
dangerous or offensive or even just kind of rude
or controversial. Which makes sense. I get it.
You need that for a tool millions of people use.
But what's the trade off? What's the cost of
being that careful? Well, they crossed as creativity,
plain and simple. Think about it like a really
talented artist, okay? But they went to this
super strict art school. And the only rule was
paint calm lakes, paint happy trees, never ever
paint anything weird or wild. After years of
that, the artist can still paint, sure. But they've
kind of forgotten how to risk anything outside
that safe little box. They're scared. So the
AIs liked that artist. scared to step outside
the lines. Exactly. It's scared of getting, you
know, disciplined for saying the wrong thing.
So it just sticks to the average, the safe stuff.
And you really see that, I think, in what I sometimes
call the lazy student effect. Huh. Yeah, what's
that? It's like the AI just looks for the answer
that's most likely to be seen as correct or just
really common, right? It scans all its data and
picks the easiest path. So you ask for a business
idea. and you get drop shipping, or start a gardening
newsletter. Every time. Yeah, that repetition,
that pull towards the average. There's actually
a technical term for it. It's called mode collapse.
Mode collapse, OK. It basically means the AI
gets stuck in one way of thinking, the most common
pattern, and it really struggles to give you
anything different. So the collapse part. Does
that mean it's like ignoring other possibilities
it knows? Kind of, yeah. It means it's not using
the full richness of all the data it was trained
on. All the answers cluster around that one super
common idea and all the interesting stuff out
on the edges. It just sort of collapses out of
view, even though the information's still kind
of in there. Honestly, I still wrestle with this
myself sometimes. You know, prompt drift, getting
stuck, outputs. I remember needing like... 20
podcast names once really unique ones. Yeah,
what happened? Terrible results just awful simple
stuff like healthy living daily I knew the AI
had better ideas somewhere inside, but I just
couldn't get it to show them to me It's frustrating.
So okay if the AI is naturally timid and its
default is well boring How do we sort of legally
give it permission to be weird to show us that
other stuff? We have to make it show its work.
It's messy thinking process We need to force
it to show us the riskier options the things
it thinks have a lower chance of being right
Okay, that leads us right into the fix then verbalized
sampling. Let's let's break that down sounds
complicated, but It's not really. It just means
you ask the AI to show it's working out, basically,
instead of just giving you the final polished
safe answer. How does just asking it to show
it's work change things? What's the mechanism?
Well, this is the fascinating part. It seems
to have this almost psychological effect on the
AI. When you force it to list multiple options,
say, you ask for five ideas. OK. And you also
ask it to assign a probability score to each
one, like it's chance of being a good answer.
That requirement forces it to look beyond the
super safe zone. Why? Why does adding a score
make it less safe? Because it has to show you
a range. To give different scores, it needs to
consider ideas with different likelihoods. It
needs variety. And that process seems to break
the constraint that normally just keeps it locked
onto the 95 % likely answer. But couldn't it
just assign 95 % to all five boring answers if
it's just a machine? Ah, good question. But it
seems the underlying architecture is kind of
designed to explore different possibilities,
different next words or tokens, when you demand
five distinct answers and a score for each. You're
telling its system that variance is mandatory.
Exactly. You're signaling that variety is part
of the goal now. And it can still satisfy its
safety training by just slapping a low score
on the weird idea. It's like it's saying, OK,
here's a weird one, but warning. Low probability.
So it gets to be weird, but only if it warns
you first. Precisely. The ice cream analogy kind
of helps here, I think. OK, let's hear it. If
you just ask an AI for its favorite ice cream
flavor, what do you get? Vanilla. Yeah. Or maybe
chocolate. The safest bet. Right. Vanilla. Safe.
But what if you ask? Give me five flavors you
might order today and score each from 0 to 100
on how likely you are to pick it. Ah, OK. then
it has to dig deeper. Yeah. Suddenly it has to
mention something like, I don't know, lavender
honey. It might give lavender honey a really
low score, maybe 5%, but the idea is out there.
And you, the user, get to decide if that 5 %
idea is actually the creative gold you were looking
for. Exactly. That's the key. Okay, so this is
a really important point, I think, for anyone
listening. Those probability numbers, the 0 .1,
the 10%, they're not like scientifically perfect
math, are they? No, not really. Especially not
in these conversational AIs. They're mostly a
tool. A trick, almost. A trick to do what? To
trick the AI into feeling safe enough to give
you something potentially wrong or unlikely.
By assigning a low number, it's basically covered
its bases. It's warned you. Right. It's like,
hey, I told you this was a long shot. Yeah. So
we, the users, don't really care about the exact
mathematical accuracy of the score. We just want
the interesting idea it unlocks. So the big realization
here is the creativity was kind of hidden in
plain sight. Our goal shifts. It's not just asking
for an answer. It's using this little phrasing
trick to force the AI to surface those lower
probability options. because that's often where
the unique stuff hides. So that naturally leads
to the practical question, right? What are the
actual words? What are these core phrases we
need to stick onto our prompts to get this scoring
and variety thing happening? Yeah, what do we
actually type? And thankfully, they're pretty
short instructions. They just demand those scores
and multiple varied responses. Okay, so the core
bits to remember, the magic words essentially,
are variations of. Generate X responses. You
pick the number X. Right, like five or maybe
three. And the other key part is with their probabilities
or with their probability scores, something like
that. Okay, maybe slightly more than eight words
sometimes, but yeah, pretty concise. So let's
look at method one. This is the simple version,
really beginner friendly. Okay. You take your
standard, maybe boring prompt, let's say. Give
me five creative title ideas for an article about
gardening. Right, the kind of prompt that usually
gets dull results. Exactly. Now, you just add
that core phrase. So the new prompt becomes,
generating five creative title ideas for an article
about gardening, each with their probability
score. That's it. And did you try this? What
was the actual difference in output? The shift
was noticeable, yeah. It's like the AI suddenly
felt freer. It gave, say, three pretty standard
titles, high scores, 90 % range, stuff like,
best tips for your summer garden. Boring sure
the usual suspects, but then it also included
one or two with much lower scores Maybe 15 %
20 % and those are the more abstract more interesting
ones something like the backyard reviva. Ah Okay,
that's much better. Yeah, and that 15 % idea
That's probably the one you actually end up using
right the one you couldn't get before make sense
So that's the easy way. What about pushing it
harder, right? That's method to the copy paste
block This is for when you want to be more explicit
maybe for more complex tasks. So you have like
a pre -written chunk of text you add. Yeah, you
keep a slightly longer instruction save somewhere,
maybe in a notes app, and you just paste it into
your prompt. And why does this longer block work
better sometimes? It tends to use more specific
language. It might use technical terms or tags,
and specifically ask the AI to include low probability
scores. It speaks more directly to the AI's kind
of internal logic. Ah, so it makes the instruction
harder for the AI to ignore or misinterpret.
Really forces that diversity. Exactly. Maximizes
the range you get back. Yeah. Let's talk about
that cold emailing example you mentioned. That
sounds like a great use case. Oh, it was really
effective. High stakes, right. You need that
email to land. Totally. So we prompted it for
five versions of a... Coal email. Crucially,
we ask for high variety in tone, targeting this
potential partner, and we ask for the probability
scores. OK, what happened? Well, version 1 was
exactly what you expect. High probability, like
0 .95. Super formal, a bit stiff, maybe easily
ignored. The safe email? The safe email. But
then we scroll down to version 5. Version 5 had
a probability score of only 0 .00. 0 .18, tiny.
Wow. And the tone was completely different, short,
punchy, really casual. It was basically like,
hey, name, got a weird idea for us, quick chat.
And that worked. That was the one that's the
email that actually got the positive response
and led to a meeting. That is fascinating because
the AI on its own probably would never have suggested
that ultra casual tone. Never. It would have
stuck with the safe, formal approach that, let's
be honest, often just gets deleted. So the human
judgment comes in to see that the low probability,
risky answer is actually the right creative choice
for that specific situation. Exactly. And whoa!
Just imagine scaling that, finding those unexpected
successful approaches across like... a billion
cold emails or marketing messages. Yeah, that's
genuinely powerful. Finding the effective outlier.
It really is. And you saw similar things with
creative writing prompts too, right? Like the
missing diamond mystery. Yeah, night and day
difference. We asked for plot twists, specifically
telling it to include low probability options
with scores. Instead of the usual, the butler
did it. Right, or the rival stole it. Those are
the high probability, kind of boring answers.
The cool low probability one it came up with.
What was it? The diamond was actually made of
ice it just melted vanished whoa okay that's
clever super clever highly unlikely in like a
gritty detective novel which is why the AI scored
it low but for fiction for a surprising twist
brilliant okay but let's tackle a potential problem
what if someone tries this they use method one
or method two ask for scores ask variety and
the AI still gives them boring stuff it's still
stuck yeah that can happen sometimes If it's
being really stubborn, there's one more thing
you can try, a way to push it even harder towards
the weird stuff. Like a final nudge? Basically,
yeah. We can use a specific statistical term
that the AI usually understands quite well. Okay,
so what's the ultimate push phrase for when you
really need the AI to get weird? You add this
instruction, please sample from the tails of
the distribution. I want highly unlikely answers.
Okay, sample from the tails of the distribution.
That sounds technical. What does it actually
mean? It's jargon, yeah, but it's pretty simple
conceptually. Think of that bell curve. Right?
Most answers, the common ones like dropshipping,
they're clustered at the peak in the middle.
Right, the high probability stuff. Exactly. The
tails are the flat bits way out on the edges
of that curve. That's where the rare, unusual,
low probability ideas live. The statistical outliers.
Precisely. So when you tell the AI to sample
from the tails, you're explicitly commanding
it. Ignore the middle. Go find the weird stuff
way out on the edges. And the AI understands
that command. Generally, yes. Large language
models have a grasp of basic statistical concepts
from their training data, so that phrase is a
pretty strong signal to force it away from the
common mode. It usually guarantees much wilder,
more diverse ideas. Okay, that's a great tip
for troubleshooting. But we should probably loop
back and reiterate that warning about when to
use this whole probability score technique. Absolutely
crucial. We've established the scores are mainly
a trick, right? To get variety. Yeah, to unlock
creativity. So use this heavily for brainstorming.
Coming up with slogans, headlines, story ideas,
marketing angles, anything where you want a range
of creative options. Some expected, some unexpected.
But when should you not use it? Don't use this
if you need a single factual undisputed truth.
Like asking for the capital of France. Exactly.
If you ask, what's the capital of France? Give
me three options with probabilities. The AI might
feel forced to invent fake capitals just to satisfy
the variety part of the request. It might say
Paris, 0 .9 probability, Lyon, 0 .08 probability.
Imaginary bill, point zero two, probability.
Precisely, because you asked for options and
scores, so stick to creative tasks where exploring
possibilities is the goal, not finding one single
right answer. Okay, good distinction. Let's try
and synthesize the big idea from this deep dive
then. What's the core takeaway? The core idea
is that AI often seems boring, not because it
lacks creativity, but because its safety training
causes this mode collapse. It defaults to the
average. Right. It gets stuck. But we can fix
that. By adding a simple instruction asking for
multiple options and their probability scores,
we essentially force the AI to look beyond the
average. We make it explore those creative tales
of its knowledge where the unusual ideas are
hiding. Exactly. The imagination, the potential
was probably there all along. We just weren't
asking the right way. We needed to ask for variety.
and probability. It wasn't a technical barrier
really it was more of a conversational one how
we ask the question. Yeah precisely so the call
to action is pretty clear isn't it? Go try it
next time you need some creative juice from your
AI use method one that simple tweak or grab that
method to copy paste block. See what happens.
See what low probability gold you might dig up
just by asking it to show its work and score
its ideas. It's definitely worth experimenting
with. And maybe here's a final thought to leave
you with. OK. If these incredibly powerful AI
systems need explicit permission from us humans
just through a specific prompt, permission to
access their own wild ideas, their own creative
outliers. What does that really imply about the
future? About the limits of their own creative
autonomy down the road. Huh. That's something
interesting to think about, needing our permission
to be fully creative. Something to mull over.
Indeed. Well, that's all the time we have for
this deep dive. Until next time. Take care, everyone.