Have you noticed this? It feels like there's
this growing idea that you need to be some kind
of elite prompts engineer now just to talk to
an AI effectively. Oh, totally. It's like the
new barrier to entry, isn't it? People think,
oh, man, I got to write these epic 500 word novels
or, you know, shell out cash for some course
to learn the secret handshake. But that's just
it. Well, it's the big mistake we want to clear
up today. Turns out. Often the absolute best
way to get a killer prompt is just letting the
AI write it for you. It's the ultimate life hack
for AI, and it doesn't take long to learn, maybe
15 minutes. So today we're diving deep into these
non -technical shortcuts, making the AI do the
hard setup work. No jargon, promise, just stuff
you can actually use. Okay, so first we'll look
at why just tossing a simple one -liner at the
AI often, well, falls flat. And we absolutely
need to define what a system prompt is. Beat.
That's like... the AI's job description. Then
we'll unpack four different methods. There's
this super simple just ask it method. Then there's
letting the AI interview you, which is cool for
complex stuff. We'll touch on optimizer tools,
some hidden gems there. Finally, how to teach
it by showing examples. Goal is, better results,
less headache. Right. Let's start with that common
failure point. You go online, you see someone
posts the perfect prompt. And it looks like they
just, I don't know, channeled it instantly. Yeah,
that's classic survivorship bias. But for props,
you're only seeing the shiny finished product.
You don't see the 20 field attempts, the slow
tweaking, adding a bit here, taking away a bit
there, that whole messy process. The real insight,
then, is starting small. It's kind of like building
with LEGO blocks, maybe. You add one little instruction,
you test it out, see if it works, then you add
the next one. Exactly. Because if you throw,
like, 10 different rules into one prompt right
at the start, and the output is garbage. You've
got no clue which of those 10 things messed it
up. It's just impossible to fix. Need that simplicity,
that structure. And that structure really starts
with that term you mentioned, the system prompt.
Sounds a bit technical, but it's key. Think of
it like this. The AI is an actor you just hired.
The normal prompt is the script for today's scene,
the specific task you want done right now. Like,
tell me a joke. The system prompt, though, that's
the actor's character sheet. It's fixed. It tells
them who they are, what their overall role is,
the hard rules they always follow. Beat. It's
their core identity for the whole play. That
chef analogy from the sources nails it. You're
hiring a chef. That's the AI. You set the system
prompt up front. OK, you're an Italian chef,
strict vegetarian only. And listen, never use
garlic. Ever. Right. So now, when you give the
normal prompt, maybe just, what's for dinner,
the AI chef has to answer within those rules.
Yeah. You get a great vegetarian, garlic -free
Italian suggestion. That fixed guide does all
the work. OK. So where do you, the listener,
actually find the setting? Is it buried somewhere
complicated? No, not usually. It's generally
in the main settings or setup area. In Chat GPT,
look for custom instructions. For the bigger
models, If you use their developer platforms
like OpenAI Platform or Anthropic Console, it's
right there waiting for you. So if the system
prompt is that fixed long -term role... What's
the biggest risk if someone just skips that step,
if they don't give the AI its role? Well, the
AI might just go off the rails, suggest things
totally outside what you need. Like suggesting
a steak recipe when you wanted that vegetarian
pasta. Okay, so we know we need this fixed identity,
this system prompt. How do we get the AI to write
a good one for us? Sounds like we need a way
to choose our approach. Yeah, there's a sort
of decision matrix. First up, the easiest one.
The simple method. This is perfect for basic,
pretty clear tasks. You need a quick email drafted,
fine, when the stakes aren't super high. And
the opposite end. That's the complex method,
what we call the reverse interview. Use this
when the task is really important, like maybe
drafting a business plan. Or, crucially, when
you kind of know what you want, but you're not
totally sure what details the AI actually needs
from you to do a good job. And squeezed in the
middle. Method three. The optimizer tools. These
are great for taking a prompt you've already
written, one that maybe sort of works, but is
messy, and just cleaning it up, making it more
efficient. Got it. Let's dig into method one
first. Just ask the AI to write its own system
prompt. Sounds like the quickest win if your
goal is clear. It is, yeah. But you got to ask
the right way. There's a template that works
really well. You basically say, hey, AI, I want
you to create a system prompt for model name,
like GPT -4. My goal is to describe your task
clearly. Can you research the best ways to prompt
for this as a put today's date? Then write me
that system prompt. Okay, hold on. Why specify
the model like GPT -4 versus CLOD -3? Does it
make that much difference? It actually does.
Different models have their own little quirks,
their own best practices for how they like constructions.
Telling it the model lets it tap into its specific
internal knowledge base for that engine. You
get better advice. And the date. Why at today's
date? Ah, that's super important because prompting
techniques change fast. What was best practice
six months ago might be outdated now. Putting
the date forces it to use the very latest information
it has. Right, keeps it fresh. OK, the coffee
shop example in the sources showed this well.
Asking simply resulted in a prompt that defined
the AI's role. Okay, you're a social media copywriter.
It set the brand voice be warm, authentic, and
added rules. Keep captions short, use hashtags,
no jargon. Yeah, it builds that structure automatically.
It even included a line like, avoid cliches such
as best coffee in town. I wonder, though, why
get that specific? Isn't the AI supposed to be
creative? Aren't we boxing it in too much? It's
about guiding the creativity. Specific constraints
like that actually help it sound more authentic,
less like generic marketing fluff that screams,
AI wrote this. It avoids the cliches everyone
else uses. OK, that makes sense. Let's shift
gears to the high -stakes stuff. Method two.
The reverse AI interview. This is for when it's
critical, or maybe the idea is still a bit fuzzy
in your own head. Exactly. Here, you tell the
AI to interview you, to ask questions until it
fully gets what you need. But here's the absolute
key instruction you must give it. Tell the AI
to ask only one question at a time. Ah, okay.
Because otherwise... Otherwise you get hit with
a wall of text, like five questions bundled together,
and it just kills the conversation. It's overwhelming.
Forcing it to go one question, then you answer
the next question. that lets the understanding
build step by step. That feels really useful.
It's got two big benefits. First, obviously the
AI gets super specific details about your situation.
Second, it actually helps you get clearer on
what you want. Sometimes you don't fully know
until the AI asks that one perfect question you
hadn't considered. You know, I'll admit, I still
wrestle with palm drift myself, especially when
starting a big new project. Just nailing down
that initial scope, getting it really defined
before the AI starts generating, that can be
genuinely tricky. Oh, for sure. The study tutor
example illustrates this perfectly. The AI asks
something broad first, like, okay, what grade
level and subject? You answer. Then it narrows
down. Got it. What's the preferred learning style?
Visuals, reading, hands -on practice. It keeps
digging until it knows the real goal. Are we
just trying to pass the test, or do we want,
like, deep understanding? OK, let's jump ahead
slightly to method four for a sec, because it
feels related to getting the feel right. Method
four is improve with examples. Yeah, this is
technically called few shot prompting. Fancy
name, simple idea. You just give the AI a couple
of good examples of what you want it to do. You
show it the pattern. Like the new employee analogy
again. You wouldn't just say, hey, write professional
emails. You'd probably show them two or three
emails that are perfect and say, make it sound
like this. Exactly that. So if you want the AI
to say, turn messy notes into polished sentences.
You give it a few pairs. Here's a messy note,
and here's the professional sentence I want.
It learns the style, the tone, the formatting,
just from those examples. So follow -up question
then. If the AI's tone is just off, maybe too
formal or too robotic, is using method four,
giving it example is usually the fastest way
to fix that. Faster than trying to write more
rules in the system prompt. Oh, absolutely. Examples
teach tone and style. way faster and more effectively
than trying to describe it abstractly with rules
alone. Show. Don't just tell. OK. Looping back
to method three, then. Using those free prompt
optimizer tools. This is for when you've got
a prompt. Maybe it kind of works, but it's just
messy. Right. And these tools are often built
right into the AI platforms themselves. OpenAI
has its playground, which you find at platform
.openai .com. And the trick here is you can actually
use method one inside the playground. You feed
it your messy prompt and ask it to optimize it.
Interesting. So I take my kind of clunky prompt,
something like plan healthy meals for the week,
no fish. I put that into the playground and then
I asked GPT -4 to rewrite that prompt using its
own best practices. What kind of changes would
I typically see? You'll see much better structure.
The AI will likely add clear sections using things
called delimiters, often just a hash symbol,
hashtag to separate different parts of the instruction.
It'll make vague terms specific like changing
healthy to maybe focus on lean protein and whole
grains. And I noticed in the source material
it often adds instructions about the output format
too, like present the answer as a table. Yes,
that's huge for consistency. It tells the AI
exactly how you want the information back. Whoa.
Okay, imagine the kind of precision you can get
when you start telling it how to structure the
output, like use a table or use these specific
hashtag section markers. That feels like a big
step up in control. It really is. It's about
making communication with the AI super clear,
which leads to way better, more reliable results,
especially if you're doing it often. Now, Anthropix
platform, the workbench for Claude, it does something
similar, but it uses XML tags, things like text
and instruction instead of hashtags. Why the
difference? It's basically just a different formatting
convention they prefer. Those tags, like text,
serve the same purpose. They help Claude clearly
separate your instructions from the actual content
or context you provide. It just helps the model
organize the input better and avoids confusion.
Think of them like labeled folders for the AI's
brain. Mid -roll sponsor, read placeholder. All
right, so we've got these methods for creating
great system prompts, but you mentioned testing.
A prompt isn't really done once the AI writes
it, is it? No way. You absolutely have to test
its durability, its robustness. A truly good
prompt needs to work across a few different but
related tasks. So take that coffee shop prompt
we talked about. Okay. Don't just test it with
one type of post. Try it for a cheerful Happy
Monday Latte post. Then try it for something
more formal, like announcing an early closing
time. And maybe try something a bit quirky or
funny, like a post about a customer who dramatically
dropped their coffee. See if it handles all three
scenarios well. And if it fails one of those
tests, say the early closing post sounds way
too casual. Then you tweak the rules in the system
prompt. You might add a line like, adjust tone
based on the seriousness of the message. Or you
could even use all -cap PS for really crucial
rules like important keep all social media posts
under 50 words Don't be afraid to emphasize and
then once it passes the stress test then you
save it Seriously start building your own prompt
library doesn't need to be fancy a simple text
file or note is fine Just give each prompt a
clear name Maybe note its goal and paste in the
full system prompt text that you know works.
This will save you so much time later Okay, let's
tackle the troubleshooting. What are the most
common headaches people run into when a prompt
isn't quite working, and what are the quick fixes?
First big one. The AI just seems to ignore a
rule you gave it. Ah, the classic. Quick fix.
Try moving that specific rule to the very end
of your system prompt. Models often pay extra
attention to the last instruction they read.
Worth a shot. Okay. Problem two, the output format
is all over the place. Sometimes it's a list,
sometimes it's a paragraph, sometimes it's something
else. Inconsistency, yeah. The fix here is usually
just being hyper -specific in your prompt. Don't
just say, make a list. Provide the answer is
a bulleted list. Ensure each bullet point starts
with a capital letter and ends with a period.
And remember method four, giving examples. That's
really powerful for teaching specific formatting.
Got it. And the third common one, the AI just
sounds like a robot. stiff, unnatural, no personality.
Right, the robot voice problem. The fix is defining
the desired tone much more clearly than just
saying Be friendly. Get really granular. Think
about how a helpful friend talks. So you might
add instructions like write in a warm, encouraging
tone like a helpful friend offering advice. Use
simple, everyday words. Avoid jargon. Limit emojis
to maybe one per response. Treat it like giving
stage directions to an actor. So putting it all
together in a workflow, let's use that vocabulary
tutor example again. You'd maybe start with method
one, the simple ask, right? Make me a prompt
for vocabulary tutor. Yep. Then you test it.
And maybe you find, ooh, the words it's giving
me are way too simple. So then you'd maybe use
method four, you'd add a few examples into the
system prompt, showing the kind of words you
want, say, B2 level vocabulary words. Exactly.
Add those examples, test it again. Oh, perfect.
Now it's giving challenging but appropriate words.
Then you save that finished tested prompt to
your library. It's that loop. Ask, create, test,
mad, adjust, bash, test, Dave. OK. So wrapping
things up. The big idea here, the main thing
we hope you take away today, is this. You really
don't need to become some kind of coding wizard
or prompt engineer in the complex sense. That's
kind of the old way of thinking. Yeah, the real
shortcut, the smarter way, is learning how to
guide the AI to define its own best identity.
How to get it to write the most effective instructions
for itself based on your goal. And we've walked
through four pretty clear methods to do that.
You can simply ask the AI. You can let the AI
interview you for complex things. You can use
those built -in optimizer tools. Or you can teach
it by showing examples. The underlying principle
for all of them, though, is start simple. Test
it. Iterate. And remember, that structure how
you organize the prompt is often just as important
as the content itself. Beat. Which makes you
wonder, since these AI models are getting so
much better at understanding how we prompt them,
What happens when the models start proactively
suggesting which of these four methods you should
use for the specific thing you're trying to achieve?
Like, the AI itself becomes your prompt strategy
coach. That's a really interesting thought for
the future. Definitely something to explore another
time. But for now, the challenge to you, the
listener, is this. Just pick one method we talked
about today. Maybe the simple ask, if you have
a clear goal. Or try the reverse interview, if
you're feeling adventurous. And just try creating
one really solid, durable system prompt for something
you do regularly. Yeah, give it a go. See what
a difference having that clear AI -generated
structure makes to your results. Bet you'll be
surprised. Alturo music.