Okay, so if you're already using AI tools like
ChatGPT, Gemini, Claude, you know how powerful
they can be. Absolutely, they're game changers.
But how often have you tried to get something
really specific, something strategic, and the
answer comes back sounding super confident, but
it's... Well, it's just off. Doesn't quite hit
the mark. All the time. It's a really common
frustration. Yeah. And you start wondering, is
it me? Am I not explaining it right? Right. Or
maybe you think you need a fancier tool or something.
Exactly. But what if the real issue is something
more fundamental? Some experts are saying it's
like a language barrier between us and the AI.
Could you maybe unpack that a bit? What does
that barrier look like from the AI side? That's
a great way to put it, a language barrier. Because,
yeah, we're speaking human with all our nuance
of assumptions, and the AI is processing based
on, well, math and patterns. The translation
definitely gets lost sometimes. And that's exactly
where prompt engineering comes in. basically
the bridge across that gap. It's not just about
asking questions. It's more like the science
of structuring what you tell the AI. The science.
Yes, structuring the input so you can guide it
towards the answers you actually need, answers
that are accurate, reliable, and genuinely relevant.
So our goal today is really to help you learn
how to speak that AI language, and crucially,
how to trust the output more, because you'll
understand why it's saying what it's saying,
and maybe more importantly, when it might just
be, well, guessing. Right, knowing when it's
confident versus when it's just sounding confident.
Precisely. So our mission for this deep dive
is simple. take you from feeling maybe a bit
frustrated or misunderstood by AI to really getting
how to communicate with it effectively, like
moving beyond just basic commands to using it
as a proper strategic partner. Yeah, getting
past the party tricks to the real power. Exactly.
We want you to turn that confident guesser into
a collaborator you can actually rely on. So let's
dive in. OK. So to really get good at talking
to AI, it does help to understand a little bit
about what's going on. you know, under the hood.
Right, because it can feel quite magical sometimes,
almost like it's thinking. It really can. Yeah.
But underneath it all, even though it seems unpredictable,
its operations are all based on strict mathematical
rules. It's incredibly complex, sure, but its
calculations, not consciousness. OK, so math,
not magic. Exactly. And the core concept is something
called token probability. Tokens, right. I've
heard that term. Yeah. So when you type a command,
like a write me an email, The AI doesn't understand
that request like a person would. Instead, it
breaks your words down into these little pieces,
tokens. And then instantly, it starts calculating
the probability of what the very next token should
be. Based on the billions of text examples it
learned from during its training, it's predicting
the most statistically likely next word or part
of a word. So it's always just predicting the
next piece of the puzzle based on everything
it's seen before. Essentially, yes. It's sequence
prediction on a massive scale. OK, so if it's
just predicting the next word, how does that
lead to it sometimes making stuff up so confidently
or just giving really generic, bland answers?
Yeah, that's the direct consequence of how it
works because it's predicting based on probability
and patterns from its training data. It's fantastic
at generating text that sounds right, that flows
well. But that same mechanism means it can generate
something that sounds totally plausible, but
is factually wrong. It's just following the most
probable word sequence. It's not checking facts
against the real world. So it doesn't know truth,
it just knows common word patterns. Exactly.
It knows patterns. And if you don't give it clear
structure, it's like imagine spinning a giant
roulette wheel with millions of possible text
patterns on it. OK. You say write an email and
the wheel spins. Will it land on business email,
personal email? sales pitch, maybe even a breakup
email pattern. It's literally guessing based
on the fragments you gave it. Wow, okay. That
roulette wheel image really clicks. But. Yeah.
When you provide structured input, you become
the one guiding the ball, leading the AI right
to the specific pattern you actually want. You
stop it from just wandering around the possibilities.
Right. So we need to stop throwing random words
at it and start giving it more of a blueprint.
But what does that structured input data actually
look like? How do we build that blueprint? That's
the million -dollar question. And it brings us
to the core of practical prompt engineering.
We're going to walk you through what we call
the gold standard for getting consistently great
results. It's a six -component framework for
basically every prompt you write. Six components.
OK. Is this something proprietary? Not at all.
In fact, it aligns really well with the kind
of principles Google and other major AI labs
teach in their advanced courses. It's just a
structure that, honestly, most everyday users
haven't really been taught or haven't explored
properly. OK, cool. So let's make it concrete.
Let's use an example. Say you're running an online
bookstore, you've got a new detective novel coming
out, and you want a really killer social media
post for it. Not just new book here, but something
that actually creates buzz. How would we use
this framework? Perfect example. OK, so we build
the prompt piece by piece, component one, role.
You need to tell the AI who it should be. Assign
it an identity. Exactly. Stop it from being a
generic chat bot. Make it an expert. So for our
bookstore, you'd start with, you are a content
marketing expert specializing in the book publishing
industry. Ah, OK. So it adopts that persona.
Right. It accesses the patterns of vocabulary
associated with that role. Second component,
context. Give it the background info it needs.
The more detailed, the better. Makes sense. So
for the book, I'm preparing to launch a new detective
novel. It's written by a Vietnamese author. The
target audience is young readers, say 18 to 30,
who are into mystery and thriller genres. And
they're mainly active on Facebook and Instagram.
That's pretty specific. Author, background, audience,
platforms. Yep. Details matter. Third component,
the task. Be crystal clear about what you want
it to do. No vagueness. OK. For our post. Write
a promotional social media post for this book.
The main goal is to create curiosity and encourage
readers to pre -order it. Direct, unambiguous.
Got it. Task is clear. Fourth, format. This is
crucial. How do you want the output to look?
Word count, bullet points. Emojis. Oh, right.
The actual structure of the response. Exactly.
So you'd add, present the output as a Facebook
post, roughly 150 words. It must include a shocking
opening line, three bullet points highlighting
the most intriguing plot elements without spoilers,
and a clear call to action, a CTA to pre -order
the book. Use a placeholder for the link. Wow.
OK. Super prescriptive. It helps avoid randomness.
Fifth component, rules. Set the guardrails. What
should it do? And just as importantly, what should
it not do? The do's and don'ts. Do. Use a captivating
and mysterious tone. Feel free to use appropriate
emojis. Do not reveal any major plot spoilers.
Avoid using overly formal or academic language.
This helps prevent unwanted results. OK. Keeps
it on track. And finally, the sixth component.
This one's like the secret sauce that many people
miss. Examples. Show. Don't just tell. Exactly.
You can actually show the AI what good looks
like by providing an example of something that
worked before. Oh, interesting. So you could
add. Please reference the tone and style from
this previous successful post. Then you'd paste
the text of that post here. Ensure the new post
is also energetic, relatable, and really drives
curiosity. This helps enormously with brand consistency
and getting the feel right. That is powerful,
giving it a direct reference point. It really
is. OK, so let's just pause and think about the
difference here. Most people, myself included
probably, would just type, write a promotional
post for a new detective novel. Right. And you'd
get something. Okay, maybe. But likely, very
generic, maybe a bit flat. Could apply to almost
any book. Yeah, totally forgettable. But using
that full six -part prompt, I mean, the level
of detail, the guidance, the result has to be
completely different. It's night and day, honestly.
I remember struggling with getting marketing
copyright, using AI early on, just getting bland
stuff back again and again. Me too. Felt like
pulling teeth. Yeah. If I'd known this framework
back then... It just takes so much of the guesswork
out of it for us. It turns the AI from this general,
slightly unreliable assistant into a focused
expert for that specific task. It really elevates
the interaction. Okay, so that six -part framework
is foundational. Absolutely. That's your starting
point for reliable results. So now that we have
that foundation, let's get into some of the more...
advanced techniques, the kind of stuff that really
separates casual users from the pros who get
amazing results consistently. Okay, let's do
it. The first one is really clever. It's called
the confidence verification mechanism. Confidence
verification, okay. The big challenge we mentioned
earlier is that AI always sounds confident, right?
Even when it's basically making things up, hallucinating.
Right, which can be dangerous if you just trust
it blindly. Yeah. Leads to embarrassing mistakes.
Totally. So the solution is pretty ingenious.
You actually force the AI to rate its own confidence
level for the information it's giving you. You
ask it how sure it is. Exactly. You add a specific
instruction to your prompt, especially for important
tasks. Something like, for each key statement
or recommendation you make, please rate your
confidence using this scale. virtually certain
that's like 95 % plus confidence, highly confident
80, 95%, moderately confident 60, 80%, or speculative
low confidence below 60%. And briefly explain
why you've given that rating. Whoa. That is incredibly
useful, but hang on. If the AI can hallucinate
facts, can we actually trust its confidence rating
about those facts? Isn't there a risk it just
confidently tells you it's confident about something
wrong? That's a really short question, and it
gets to the heart of it. The confidence rating
isn't about the AI knowing objective truth in
a human sense. It's about the AI reflecting the
certainty level derived from its source data
for that specific piece of information. Ah, okay.
So it's rating its source material in a way.
Precisely. Think about a financial analyst scenario.
You ask the AI to analyze two companies. It might
say its projection for company A's revenue growth
is highly confident. Why? Because it's based
on data from a recent verifiable quarterly reported
process. Okay. But then it might say its statement
about company B's upcoming product launch succeeding
is only moderately confident. Why? Because the
source for that was maybe just an unverified
press release or some analyst speculation it
found. I see. So it's flagging the reliability
of the input information it used to generate
the output. That gives you context. Exactly.
It tells you what's grounded in solid data versus
what's more speculative. That difference is critical
for making actual decisions based on the AI's
output. That's a game changer for critical tasks.
OK, brilliant. What's next? OK, so we've talked
about getting the AI to tell us how confident
it is. But what if? What if the AI could actually
help you write the perfect prompt to begin with?
Wait, the AI helps write the prompt for the AI?
Yes. This is where the AI prompt assistant technique
comes in. It's genuinely revolutionary. Okay.
Mind blown already. How does that work? Two main
ways. First, say you're starting completely from
scratch. You have a goal like, I need a three
month Instagram content strategy for my sustainable
fashion brand. But you have no idea how to structure
the prompt for the best results. Yeah, happens
all the time. You just tell the AI, OK, I want
to build this three month content strategy for
my sustainable fashion brand on Instagram. My
goals are X and Y. Can you please write me the
optimal prompt that I should then use to get
the most detailed and effective plan from you?
And it will write its own instructions. Yes,
it will generate a beautifully structured prompt
for you, probably using that six -part framework,
specifying the role it should take, the context
it needs, the tasks, the format, everything.
It knows what it needs to do its best work. That's
incredible. It saves you figuring out the perfect
prompt structure yourself. Totally. Okay, the
second approach is for when you already tried
a prompt, but the results were, you know, You
can go back to the AI and say, look, I use this
prompt. Plan some Instagram content for my handmade
pottery business. Give me ideas. But the results
were really weak. Can you analyze my original
prompt, tell me why it was probably ineffective,
and then improve it so I get a much better outcome
next time? And it critiques your prompt. It does.
It'll point out what was missing. Like maybe
you didn't specify the target audience or the
brand style or your specific goals or the format
you wanted. And then it will give you a revised,
much stronger prompt structure to use. It literally
becomes your prompt writing coach. Wow. OK. That
changes the game from just using it as a tool
to using it as a collaborator in the process
itself. Exactly. It partners with you to get
better results. All right. What else have you
got in the advanced tool tip? Next up is something
really crucial, especially now with different
versions of models. available, the secret to
choosing the right model. Like choosing between
GPT -4 or Claude Opus or Gemini Pro, that kind
of thing. Precisely. It's not just about which
company's AI, but often which specific version
or model within their offerings. Most people
just use the default, but pros know that different
models are optimized for different tasks. Picking
the right one massively impacts output quality.
Okay, so what are the main types? Broadly, you
can think about it like this. You've got creative
models, think GPT -4 in some modes, Claude III
Opus maybe? These excel at tasks needing emotional
nuance, creative writing, brainstorming, generating
novel ideas. Right, for the artsy stuff. Kind
of, yeah. Then you have analytical and reasoning
models like Gemini Advance or GPT -4 Turbo. These
are your powerhouses for complex, problem -solving,
crunching data, logical deduction, detailed analysis.
Heavy lifters. Exactly. And then you often have
fast and light models, maybe Claude III, Sonnet,
or Gemini Pro. These are great for quick, straightforward
tasks where speed is important and deep complexity.
isn't the main goal, like quick summaries or
answering simple questions. OK, that makes sense.
It's like having different tools in a toolbox.
That's the perfect analogy. Yeah. You wouldn't
use a sledgehammer to hang a picture frame, right?
Hopefully not. Can you give a concrete example
of how choosing the wrong model messes things
up? Sure. Let's go back to that legal document
example. OK. Imagine you need to summarize a
dense five -page contract and identify potential
risks. Okay, important task. If you feed that
into a model optimized for analytical reasoning,
it'll likely give you a precise summary, correctly
flag the key clauses, and accurately pinpoint
the potential risks and ambiguities. What you
need. Right. Now run the exact same prompt on
a model primarily designed for speed or maybe
creative writing. You might get a summary that
sounds okay on the surface, but it can easily
miss crucial nuances, misinterpret a subtle legal
point, or fail to identify a significant risk
because its strength isn't in that kind of deep
logical parsing. Wow, okay. So the same prompt
can yield dramatically different quality and
reliability depending on the engine running it.
Absolutely. Using the right tool for the job
is paramount, especially for high stakes tasks.
It's not just about the prompt. It's the prompt
plus the right model. Got it. Use the right knife
for the job. What's next? Okay, technique number
four, the self -improvement loop. This one feels
like unlocking a hidden superpower. Self -improvement
for the AI. Yes. instead of you getting a mediocre
result, getting frustrated, and trying to rewrite
the prompt from scratch. Which I've definitely
done many times. We all have. Instead, you ask
the AI to critique its own previous response
and then improve it based on that critique. Wait,
really? You tell it to grade its own homework
and then do it again better? Exactly. It sounds
crazy, but it works incredibly well. Imagine
you use the six -part framework to get, say,
a draft for a 60 -second TikTok video script
about a language learning app. OK, you get a
first draft. Probably decent. Right, probably
OK. But then you follow up with this instruction.
Analyze your previous response. Identify three
specific weaknesses in the script. Then... rewrite
the entire script specifically to address those
three weaknesses. Let's do this process three
times in total, focusing on different potential
issues each round. Three times. Wow. Yeah. So
round one, the AI might say, OK, the hook wasn't
shocking enough. The call to action was a bit
weak and the pacing felt slow. Then it rewrites
the script, fixing those things. OK. Then you
run the instruction again. Round two, it might
analyze the new script and say, all right, the
benefits are listed too dryly, there's no humor,
and the example scenario isn't very relatable.
Then it rewrites it again, addressing those points.
And round three. Maybe it focuses on character
voice or visual suggestions or tightening the
language further. Each iteration makes the script
stronger, moving it from good to great to potentially
excellent. That's amazing. It's like having an
internal quality control loop built right in.
without me having to pinpoint all the flaws.
Exactly. It takes the burden of detailed critique
off you and leverages the AI's own analytical
capabilities to refine its output. It's incredibly
efficient for improving quality. That's a technique
I'm definitely trying later today. Number five
is super simple but incredibly effective. Step
-by -step thinking. You might also hear this
called chain of thought prompting in some more
technical circles. Step -by -step thinking. What's
the magic phrase? Literally just adding the instruction
think step by step add that phrase to pretty
much any prompt Asking for something strategic
complex or requiring logical reasoning just think
step by step. That's it That's often enough.
Its power is seriously underestimated study after
study and just practical experience shows that
prompts using this consistently deliver better,
clearer, more reliable results, especially for
things like business plans, marketing strategies,
complex analysis, anything where the reasoning
process matters. Why? What does adding that phrase
actually do? It essentially forces the AI to
externalize its internal thought process. Instead
of just jumping to the final conclusion or recommendation,
it has to first outline the logical steps it's
taking to get there. Ah, so it shows its work,
like in math class. Exactly like showing its
work. Let's take that cold brew coffee launch
plan example. A basic prompt might just give
you a list of tactics. Okay, maybe useful. Right,
a list. But add think step -by -step to that
prompt. Now, the AI will likely structure his
response differently. It might start with, okay,
step one, analyze the target audience for cold
brew. Then step two, define the product positioning
and key messaging. Then step three, identify
optimal communication channels. Then step four,
develop a detailed 30 -day content calendar.
You see the structure? Yes. It's laying out the
whole strategic process, not just the final action
items. You can follow the logic. Precisely. Each
step builds on the last. It moves the output
from just generic advice to a well -reasoned
structured strategic plan. It's much more robust
and trustworthy. That's incredibly valuable for
anything strategic. Okay, one more advanced technique.
Last one for today, the priming technique. This
is about warming up the AI's brain on a topic
before you ask your specific question. Priming,
like getting it ready. Exactly. Instead of diving
straight into a very specific request, you first
ask a broader, related question. This activates
all the relevant knowledge and concepts within
the AI's network related to your topic. Okay,
how does that work in practice? Let's say your
goal is to create an email marketing campaign
to improve customer retention for a SaaS product.
Okay, specific goal. Before you ask for campaign
ideas, you prime the AI. You have something broader,
like, what are the key psychological principles
that are known to increase customer retention
and build brand loyalty, especially in a subscription
-based business model? Ah, so you're asking for
the theory first. Right. The AI will then explain
concepts like, you know, the endowment effect,
reciprocity, loss aversion, the importance of
community, self -determination theory, stuff
like that. It activates that knowledge. Its brain
is warmed up on retention psychology. Now what?
Now you hit it with your specific request. referencing
the priming. Okay. You are an expert in customer
retention marketing. Based specifically on the
psychological principles you just outlined, propose
five concrete strategies I can use in my email
campaign to reduce our customer churn rate. Please
present this as a structured plan, maybe with
an example email subject line for each strategy.
Wow! And the result? The results are usually
incredibly impressive. Because you primed it,
The AI doesn't just give generic tips like offer
discounts. It starts applying those deeper psychological
principles directly to your problem, leading
to much more sophisticated, insightful, and genuinely
strategic recommendations. It connects the theory
to the practice because you prompted it to do
both. Exactly. It leads to much deeper thinking.
These advanced techniques They really shift the
interaction, don't they? It's less about just
getting an answer and more about guiding the
AI to generate a high -quality, reliable strategic
answer. That's the whole point. And it brings
us to a really important mindset shift. A common
trap people fall into is they finally get one
prompt to work well, get a good result, and they
stop there. They celebrate the win. Yeah, relief.
Finally got something useful. Right. But true
prompt engineering, building real, reliable systems,
involves more. It's about testing that prompt,
seeing where it might fail under different conditions,
refining it, tweaking it until it's basically
foolproof for that specific task. Moving from
one -off successes to repeatable processes. Exactly.
And that's why we strongly advocate for creating
your own personal prompt library. A library of
prompts that work. Yes. As you develop and test
prompts, using the framework and these techniques,
the ones that consistently deliver excellent
results for specific tasks, save them. Make sense.
Maybe by category like marketing, sales, data
analysis, code generation, writing, whatever
you do. Use a tool like Notion, Google Docs,
Evernote, whatever works for you. And the benefit.
The benefit is huge. Over time, you build this
arsenal of tested, reliable prompts. Need to
write a certain type of email. Boom, grab your
prompt. Need to analyze sales data in a specific
way. Grab that prompt. You're not starting from
scratch or guessing each time. You're handling
recurring tasks in seconds. Reliably. Takes the
friction out of it and ensures consistency. I
like that. It's a massive time saver and quality
booster long term. OK, and what about for really,
really critical stuff like decisions with major
consequences? Is there another layer of quality
control? Absolutely. For those truly critical
decisions, maybe a major business strategy pivot
or a sensitive communication. We recommend what
you could call advanced quality control. Sounds
serious. It involves a bit more effort. but it's
worth it for high stakes. Yeah. The idea is run
your carefully crafted prompt not just on one
AI tool, but across multiple different ones.
Ah, so run it on Gemini and Claude and ChetGPT,
for instance. Exactly. Get responses from several
of the top models using the same robust prompt.
They'll likely have different strengths. It might
offer slightly different perspectives or solutions.
OK, so now you have multiple outputs. Then what?
Then you take those different responses and feed
them back into one of the AI tools. preferably
a strong analytical one, and you ask it to analyze
and compare all the responses. Ask the AI to
judge the other AIs. Sort of. Ask it to synthesize
the common themes, identify the unique strengths
or weaknesses of each response, and perhaps even
recommend the best overall approach or combine
the best elements. That's meta. But I can see
how comparing multiple expert opinions from different
AIs and then having one synthesize them would
lead to a much more robust final decision. It
really does. It moves you beyond just hoping
one AI got it right towards building a system
that leverages multiple perspectives for maximum
reliability. It's the difference, as you said,
between celebrating single wins and building
truly effective dependable systems. Wow. OK.
So we've covered a lot how AI thinks the foundational
framework, advanced techniques, building systems.
What's the immediate takeaway for someone listening
right now? What should they do first? Great question.
Let's make it super actionable. Here's your plan
straight up. First, pick one single task, something
you regularly ask an AI to do. Could be writing
certain emails, drafting social media content,
summarizing reports, analyzing some data, whatever
is common for you. Just one task to start, OK?
Second, build a prompt for that task using the
six -part framework we went through. Roll, context,
task, format, rules, examples. Take the time
to really flesh out each component. Got it. Build
the foundation. Third, layer on maybe two or
three of those advanced techniques we discussed.
Maybe add the confidence verification or think
step -by -step or try the priming technique if
it fits. Enhance the prompt. Fourth, and this
is key, test it. Run the prompt several times.
See what you get back. Does it consistently deliver?
Where does it fall short? Tweak the prompt. refine
it until the output is genuinely useful with
only minimal editing needed from you. Iterate
and improve. And finally, fifth, once you have
a prompt that reliably works for that task, save
it. That's the first entry in your personal prompt
library. Start building the library. Exactly.
Just doing this process for one recurring task
will fundamentally change how you see and use
AI. It puts you way ahead of the curve into that
small group of people who really understand how
to leverage these tools professionally. That
feels achievable. Start small, build confidence,
see the difference. That's the way. So wrapping
up, it feels like the core message here is pretty
clear. Yeah, I think, ultimately, the real difference
between someone who just dabbles with AI and
someone who uses it expertly isn't necessarily
about having access to the fanciest, newest tools.
It's about how they communicate with those tools.
It's the quality of the conversation. It really
is a communication skill. And hopefully, everyone
listening now has a much better grasp of how
AI actually works that probabilistic prediction
engine. Right, understanding the why behind the
output. And knowing how to get it to show its
work with context. levels or step -by -step thinking,
how to make it improve its own work, how to prime
it for deeper insights. Even though it always
sounds confident, you now know how to look behind
the curtain a bit. You're equipped to have a
much more intelligent, productive conversation
with it. So the call to action is simple. Pick
that one task. Build that one great prompt using
these techniques. Test it. Experience that night
and day difference for yourself. I guarantee
once you see what's possible, you'll never want
to go back to just basic vague prompting again.
It opens up a whole new level of partnership
with these tools. So maybe the final thought
for everyone listening is this. The AI revolution
isn't just about the fact that these powerful
tools exist and are accessible. The real revolution
for you personally and professionally comes when
you learn how to wield them like an expert. So
what could you truly achieve if your AI stopped
being a sometimes helpful, sometimes frustrating
tool and instead became a genuinely reliable,
strategically intelligent partner in your work
in your biggest projects?